revised-11_tiwari_ch011_449-492

449

Ashutosh Tiwari, Rui Wang, and Bingqing Wei (eds.) Advanced Surface Engineering Materials, (449–492) © 2016 Scrivener Publishing LLC

11

Advanced Titanium Surfaces and Its Alloys for Orthopedic and Dental Applications Based on Digital SEM Imaging Analysis

Sahar A. Fadlallah1*, Amira S. Ashour2 and Nilanjan Dey3

1Materials and Corrosion Lab (MCL), Faculty of Science, Taif University, Taif, Saudi Arabia; Chemistry Department, Faculty of Science,

Cairo University, Giza, Egypt 2Department of Electronics and Electrical Communications Engineering,

Faculty of Engineering, Tanta University, Egypt 3Department of Information Technology, Techno India College of Technology,

Kolkata, India

AbstractOver the last 10 years, the research was directed to improve the smooth tita-nium surfaces to be highly ordered nanostructures surface (tubes, pores, chan-nels, and sponges) to improve its osseointegration for a wide range of biomedical purposes. Electrochemical oxidation process in aqueous solutions was used to fabricate anodize conversion layer with nanostructure titanium oxide thin films. The porous-surface implants contributed to success the implantation surgical operations because they provide large contact area with surface roughness at implant–bone interface can help into the formation of physico-chemical bond-age with the surrounding hard tissues. Nanostructures surface morphology can be characterized by different electron microscopy. Accurate measurement of the nanostructures morphology enables the consistent characterization of their prop-erties. Consequently, intensive image analysis of the electron microscopic images can be considered an extensive stage for efficient nanostructures characterization. This morphological image analysis is consists of contours identification, image segmentation, shapes classification, and thickness measurements. The current chapter summarized, classified, and evaluated titanium surfaces developed via

*Corresponding author: [email protected]

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450 Advanced Surface Engineering Materials

anodization conditions through discussing in-vitro and in-vivo previous studies. Furthermore, the role of image processing analysis to test, evaluate, and confirm the morphology of implanted surfaces is discussed. Therefore, automated and enhanced SEM images using imaging processing analysis techniques to support the optimal preparation condition of implanted surfaces will consider as a future aspect in the field of biomaterials.

Keywords: Nanostructures titanium surfaces, anodization, electrochemistry impedance spectroscopy EIS, corrosion resistance, physiological solutions, EDX analysis, in vivo studies, in vitro studies, digital SEM image analysis, image processing, nanostructures microscopic image analysis, images segmentation, image classification, contour detection

11.1 Introduction

In the last two decades, the medical implant industry specially in the ortho-pedic, dental, and cardiac implant can improved the quality and expectancy of life of thousands of people, where the hip replacements increased 33% in a 10-year period (1990–2000) [1]; there were more than 1300 types of oral implants available in 2000 [2] and around 60,000–95,000 patients undergo implantation of prosthetic cardiac valves every year [3, 4]. These success-ful developments of the medical implant industry in the last 50 years have been achieved mainly by the combination of two important factors; the improvement in the cleaning and surgical procedures and the develop-ment of novel and successful biomaterials, which are currently defined as “materials intended to interface with biological systems to evaluate, treat, augment or replace any tissue, organ or function of the body” [5]. For large success of biomaterial implantation surgeries without undesirable rate of implant failures, research needs to look for further development could be achieved by designing biomaterials as nature does; as composite materials where bulk and surface properties are independently tailored. The bulk properties are chosen according to the functional characteristics of the implants, such as high mechanical strength, flexibility, and optical transparency; and the surface properties of the biomaterial are modified in order to fulfill the biocompatibility and biofunctionality of the implants, which depend on the biological characteristics of the medium where the implant will be placed. So far, three basic surface characteristics to improve the biocompatibility of the medical implants have been largely identified: (a) high bio-corrosion resistance in order to stand the harsh and corro-sive ambient in the human body, preventing the release of ions, debris, or the material degradation; (b) chemical inertness to avoid any reaction of

Advanced Titanium Surfaces and Its Alloys 451

the material with the chemical components in the biological ambient, and (c) bioactivity, which generally means the ability to trigger an appropriate response of the body as a consequence of the interaction with the material, such as bone growth [6].

Titanium and its alloys are known as inert materials, so their widely used in the production of biomedical implants due to their high tensile and fatigue strength, low Young modulus, and superior corrosion and wear resistance [7]. Titanium is a material with a high superficial energy and after implantation it provides a favorable body reaction that leads to direct deposition of minerals on the bone–titanium interface and titanium osseo-integration. This is due mainly to the thin oxide film TiO2 naturally formed on its surface, which is very stable and chemically inert. Moreover, they are biocompatible, providing direct bonding with bone surface and are inert in physiological environments [8]; the most important problem facing bone implantation surgery by using titanium is slow reaction of titanium with the human tissues resulting in difficulties in bone attachment. This problem is due to high processing energy of titanium, which results from melting and casting difficulties and its high elastic modulus compared to bone [9]. To overcome this problem, titanium is alloyed with nickel or with small amounts of aluminum, vanadium, or niobium to improve some of the Ti characteristics such as strength, mechanical properties, corrosion resistance, and so on. Titanium alloys most frequently used in dental and orthopedic implantations are NiTi (Nitinol), Ti-6Al-4V, and Ti-6Al-7Nb. NiTi is a material with excellent shape memory effect and super elasticity and damping capacity suitable for use in dentistry, where it is used in ortho-dontics for brackets and wires connecting the teeth [10, 11]. Ti-6Al-4V is the material used for only ~20–30% of commercially available medical devices due to Al and V may cause neurological disorders and bone dis-eases when they are released in the human body. Ti-6Al-7Nb is another titanium alloy with excellent strength and biocompatibility dedicated for surgical implants used especially for replacement hip joints but its stability effect by pH [12, 13].

Recently, to reduce the problems associated with stress generated by the incompatibility between the elastic modulus of Ti implant and that of the bone is to fabricate titanium implants with controlled porosity [14]. Nanoscale surface modification techniques introduce novel bioactive capacities nanostructures titanium scaffolds with good mechanical prop-erties (particularly, elastic modulus, and stiffness) that provide a favor-able environment for bone growth and a significant enhancement in the osteoblastic activity was produced [15]. Nanostructures can have the form of nanotubes, nanoporous, nanofibers, which have attracted researchers’


attention in different applications from delicate electronics to innova-tive medical processes. Bioactive molecules such as bone morphogenetic protein-2, and nanoparticles such as Ag, Au, Ca, and P have also been introduced onto nanostructure titanium implant surface to improve osseo-integration and antibacterial properties [16, 17].

Anodic oxidation is an electrochemical method, which can be easily implemented to produce uniform and controllable nanostructures on Ti surface [18]. It has been proved that the nanostructure surface can also promote osseointegration. For instance, osteoblasts proliferation was observed to be significantly enhanced on nanostructure titania (TiO2) sur-face in comparison to their conventional counterparts [19]. In addition, during anodic oxidation, the forces resulted from concentration gradi-ents during ionic transportation in electrolyte can benefit the incorpora-tion of inorganic and organic constituents on Ti surface, which will help to improve the osseointegration. Cathodic deposition is also an economi-cal and simple electrochemical method to introduce designed elements on metal surface. A variety of metal elements have been incorporated on metal surface by this technology including Ag, Zn, and Au. Although several chemicals and antibiotics have been incorporated on Ti surface to improve biological properties, there are few researches on the incorpora-tion by electrochemical method till now.

The surface morphology of the nanostructure titanium implant sur-faces was determined by electron microscope such as scanning electron microscopy (SEM) and atomic force microscope (AFM) obtained pic-tures showed the morphology of surface layers demonstrated the degree of roughness and the nanoparticles expectancy that incorporated onto nanostructures surfaces. The deposition or incorporation of any particles on nanostructures was confirmed by X-ray diffraction, ICP-MS, EDX, and surface-sensitive XPS measurements, which they showed a clear corre-lation between the applied sputtering parameters and the particles con-tent of the nanostructures [20, 21]. The rapid growth of technology has resulted in an enormous amount of data in the form of images, which results from advanced microscopes. In order to manage efficiently these images and extract useful information, all objects in the image need to be identified. To perform better and effective objects and surfaces identifica-tion for further analysis, the process needs to be performed automatically and without prior knowledge of the image content. Nanoparticles and nanostructures can be engineered with distinct shape, size, composition, and surface chemistry to enable new procedures [22]. Thus, morphology control on the nanoscale becomes a vital topic of interest due to these impressive effects.


Therefore, engaging the microscopy as an outstanding tool in capturing images of the nanoparticles/structures morphology is essential. However, it is difficult to assess the discrepancy in the nanostructure’s size and shape simply by observation of the microscopy images. Thus, the devel-opment of computer-assisted tool for automated detection and morphol-ogy identification of the nanoparticles/structures is an extensive research area. Contemporary nanostructure characterization and their morphol-ogy analysis have a computation intensive. Thus, automatic evaluation to nanostructure analysis and automated techniques to its characteriza-tion is indispensable for nanomanufacturing and nanotechnology. Digital image processing approaches can significantly improve the quantification of nanostructure local inconsistency with minimum time consuming and great accuracy. Various image processing/analysis techniques and algo-rithms depicted an expedient and practical tool to analyze and observe nanostructures qualitatively and quantitatively by analyzing the image structural information. For SEM microscopic image analysis/processing image segmentation and classification can be performed automatically after feature extraction step.

In the current chapter, the basic concepts involved in the modifica-tion of titanium surface and processing of thin films and bioactive coat-ing formed on it to improve its surface for excellent compatibility in human tissue with comprehensive survey of the already well-established knowledge on the topic is addressed. Along with including the image processing extensive role to analysis and identify, the morphological properties of the nanostructure images captured by electron microscope are discussed.

11.2 Titanium Implants Basic Concepts

Titanium is a widely used material for implants because of its evident bio-compatibility. Commercially distributed implants do not have a uniform surface. In fact, one is trying to provide every part of the implant with an optimized surface depending on function, mechanical stress, and duration of incorporation in human body. To produce a defined roughness of tita-nium surfaces, smoothing, abrading, and coating procedures are applied. For optimization of implant surfaces, an exact knowledge about the influ-ence of roughness on behavior of the biosphere is required. The aim of the following sections is a preferably complete and exact description of the factors effect on titanium surface and how change in surface morphology play a key role to improve the implant–bone interaction. In parallel cell


biological examinations, it is required to find out a correlation between biocompatibility and defined physico-chemical characteristics. To estab-lish suitable parameters to test the biocompatibility of structured titanium surfaces, the current chapter focused on the investigation of in-vitro and in-vivo results of multiple modified titanium surfaces prepared under a variety of circumstances.

11.2.1 Titanium Oxide as Biocompatible Coatings

The most important requirement for the long-term success of implants is the stable interface between the biomaterial and the surrounding tissue [23]. Hence, an improvement of all Ti-based materials corrosion resistance is of particular importance. In this regard, the excellent corrosion resis-tance of titanium in many environments is primarily due to the protection afforded by the passive film that is developed upon its surface. The film is based upon titanium oxide (TiO2), with a thickness of a few nanometers [24]. The naturally formed oxide on titanium is well known to be sufficient for many applications requiring low corrosion rates. Therefore, TiO2 natu-rally formed on the surface is one of the main candidates for protective coatings on Ti and Ti alloys substrates due to its thermodynamic stability, chemical inertia, and low solubility in body fluids [25]. However, the native TiO2 layer is very thin, irregular, and porous, and there is also the risk of viruses being carried by this layer. Moreover, the TiO2 naturally formed on Ti surface is not sufficient to ensure a strong chemical bond with bone tis-sue and a safe, long lifetime of the implants [26].

Consequently, efforts are made for thickening titania oxide layer along with an improvement of its surface ability to promote osteosynthesis and to increase its corrosion and wear resistance. Titania with specific structures of anatase and rutile was found to stimulate apatite forma-tion and was proved to be much more beneficial for bone growth than the amorphous TiO2 [27]. The development of nanostructures titania has allowed unprecedented access to an intimate interaction with the biologi-cal environment [19]. For bone regeneration and repair, re-creating sub-strates on a nanoscale allow scientists to probe the first level of the bone structural hierarchy [28]. Studies have shown that TiO2 films consisting of nanostructures (nanotubes, nanorods, nanosheets, etc.) exhibit high hydrophilicity, improving, thus the bioactivity and bone-bonding behav-ior of materials for implants [29, 30]. However, there is still a lot to do in the direction of improving the behavior of titanium implant biomate-rials. There have been a vast number of methods developed to produce nanoporous and nanostructure films [31–33]. Either liquid-phase [32] or


vapor-phase processing [33] can be used to produce nanostructure film materials. Within each phase, the structure of the material may be ran-dom or ordered [34]. The former leads to amorphous materials, the latter, to more crystalline-like materials. Each film can be considered as either intrinsic or extrinsic. Intrinsic or additive structures are formed directly as the film is assembling or growing much as the skeleton of a skyscraper. Extrinsic or subtractive structures are formed. As a solid mass containing sacrificial material that is either burned out, dissolved out, or milled out to yield a final structure. Finally, the film material can be either inorganic, organic, or a hybrid of both [35–37]. The aim of surface modification is not only to increase the stability of titanium implant in physiological solution but it also to enhance the antibacterial properties and to be non- cytotoxicity surface [21].

11.2.2 Nanostructures Importance

A nanostructure is a structure of intermediate size between micro-scopic and molecular structures. Nanostructure detail is microstruc-ture at nanoscale. In describing nanostructures it is necessary to differentiate between the numbers of dimensions on the nanoscale. Nanotexture sur-faces have one dimension on the nanoscale, i.e., only the thickness of the surface of an object is between 0.1 and 100 nm. Nanotubes have two dimensions on the nanoscale, i.e., the diameter of the tube is between 0.1 and 100 nm; its length could be much greater. Finally, spherical nanopar-ticles have three dimensions on the nanoscale, i.e., the particle is between 0.1 and 100 nm in each spatial dimension. As shown in Figure 11.1, there are different types of nanostructures such as nanofoames that are a class of nanostructure, porous materials, and foams, containing a significant population of pores with diameters less than 100 nm, The nanomesh is a new inorganic nanostructure two-dimensional material, similar to gra-phene, nanocomposite is a multiphase solid material where one of the phases has one, two, or three dimensions of less than 100 nanometers (nm), or structures having nanoscale repeat distances between the differ-ent phases that make up the material, Nanofibers are defined as fibers with diameters less than 100 nanometers, nanorods are one morphology of nanoscale objects. Each of their dimensions is range from 1–100 nm. Nanostructure of metal oxides can be classified into different classes depending on the number of dimensions that are nanosized (dimensions less than 100 nm): zero dimensional (0D) for clusters, nanoparticles; 1D for nanowires/nanorods; 2D for films, nanosheets; and 3D for nanocubes and other complex nanoarchitectures [38–40].


11.2.3 Natural Nanostructures

Generally, nanostructure can be defined as a system in the order of 1–100 nm in size. A nanostructure material can be defined as a material with atoms/molecules arranged in nanosized clusters, which become the constituent grains or building blocks of the material. Artificial nanostruc-tures are simulated the nature nanostructures, every natural structure is built up from substructures that are of the nanosized range. We are thus surrounded by nanomaterials. Our own body is a perfect example of a nanostructured material. Every single part of the body is built up from nanostructures which have completely different structural arrangements that decide their functionality. The DNA structure that exists within the nucleus of a cell is one such nanostructure whose sole objective is to store and replicate information. Even the energy in the system cell is stored in the form of easy-to-manipulate bonds within nanostructures formed by adenosine tri-phosphate (ATP) and adenosine di-phosphate (ADP) mol-ecules. A nanostructured membrane that forms the cytoskeleton of a cell

Figure 11.1 Microscopic top view of different types of nanostructures.

(a)Microscopic view of

nano wiresMicroscopic view of

nano pores

20 nm 200 nm

(b)

Microscopic view of nano tubes

Microscopic view ofnano cubes

200 nm

1 µm

200 nm

(a) (b)


performs yet another type of function: regulation of ion and molecular transport across the cell boundary. The role of nanostructures is hence extremely significant, and extensive research is being conducted in this field to gain greater understanding of the functionality of specific struc-tures [41, 42].

Central strategy of science has been to try and mimic nature. Manufactured water-repellent and self-cleaning surface have potential uses in daily life, agriculture, and industry. Lotus leaf is the natural nanostruc-ture which shows the above water repelling property. SEM images have revealed that the lotus leaf surface is covered with micrometer-sized papil-lae decorated with nanometer branch-like protrusions. The roughness of the hydrophobic papillae reduces the contact area between the surface and a liquid drop, with droplets residing only on the tips of the epicuticular wax crystals on the tops of papillose epidermal cells. Researchers were able to develop special technique to produce artificial material which mimics the lotus leaf. They are prepared with a simple one-step production process using ultrafast (femtosecond) laser irradiation of a silicon surface under a reactive gas atmosphere, followed by a chloroalkylsilane monolayer depo-sition. This consists of micro scale conical pyramidal asperities decorated with nanoprotrusions of up to a few hundred nanometers, as shown in Figure 11.2.

The present chapter is concerned with the human bone and nano struc-tural nature attached to it. Bones are rigid organs that form part of the endoskeleton of vertebrates. They function to move, support, and protect the various organs of the body, produce red and white blood cells and store minerals. Bone tissue is a type of dense connective tissue. Because bones come in a variety of shapes and have a complex internal and external

Figure 11.2 SEM images of the lotus leaf surface is covered with micrometer-sized papillae decorated with nanometer branch like protrusions.

(a) (b) (d) (e)

(c) (f)

5 µm

5 µm

10 µm

Natural protrusionon a lotus leaf

Arti cial silicon surface


structure they are lightweight, yet strong and hard, in addition to fulfilling their many other functions. One of the types of tissue that makes up bone is the mineralized osseous tissue, also called bone tissue that gives it rigidity and a honeycomb-like three-dimensional internal structure. Other types of tissue found in bones include marrow, endosteum, and periosteum, nerves, blood vessels, and cartilage. Bone is not a uniformly solid material, but rather has some spaces between its hard elements. The hard outer layer of bones is composed of compact bone tissue, so-called due to its mini-mal gaps and spaces. This tissue gives bones their smooth, white, and solid appearance and accounts for 80% of the total bone mass of an adult skel-eton. Compact bone may also be referred to as dense bone. Filling the inte-rior of the organ is the trabecular bone tissue (an open cell porous network also called cancellous or spongy bone), which is composed of a network of rod- and plate-like elements that make the overall organ lighter and allow-ing room for blood vessels and marrow. Trabecular bone accounts for the remaining 20% of total bone mass but has nearly ten times the surface area of compact bone. Proteins are the key molecular building blocks of such biological matter and they play a vital role in making these materials are lightweight, yet strong, elastic, and tough. One of the most intrigu-ing protein materials found in nature is bone, a material composed out of assemblies of tropocollagen molecules and tiny hydroxyapatite crystals, forming an extremely tough, yet light weight material. Figure 11.3 depicts the geometry of the nanostructure of bone, showing several hierarchical features from atomic scale to microscale [43, 44].

11.2.4 Fabrication of Titania Nanostructures

Nanostructures formation on the Ti oxide is important to improve the cell adhesion and proliferation in clinical applications [45], so researchers

Figure 11.3 Nanostructure of natural bone.

Amino acids~1 nm

Tropocollagentriple helix~300 nm

Mineralizedcollagenbrils~1 µm

Mineralizedcollagen brilswith extrabrillar~10 µm

HA crystalTC molecule

Articular cartilage Periosteum

Compact boneMarrow cavity

Epiphyseal plate

Cancellous bone

Epiphysishead

Hierarchical structure of the bone formation Compact and spongy bone

Diaphysisshaft

Epiphysis


have sought to create a superior biological response to titanium implants by modifying the surface finishing process (Figure 11.4). And faster and firmer fixation of dental implant in bone interface has been researched on coating of bioactive materials in nano surface [46]. Therefore, recent research reports concluded the scientific fact that the nanostructure sur-face oxide film with nanoscale based on titanium, and its alloys will result in very strong reinforcement of the bone response [47].

For the fabrication of nanoscale structure titanium surface, there are different methods have different capabilities to produce specific character-istics such as an examples:

• Sol–gel process, which it is consider a method for produc-ing solid materials from small molecules that is suitable for preparing different coatings (e.g., silicon and titanium oxides) on the surface of Ti-based materials. Irrespective of the nature of the film, there are two main techniques used to apply a sol–gel coating on the surface of a metallic substrate are dip and spin coating [48].

• Laser surface treatment is a thermal process that has an advantage over conventional furnace treatment. It is based on the heating caused by the light adsorption of the surface layer and the cooling ensured by the high conductivity of the material [49].

• Plasma electrolytic oxidation is a relatively inexpensive and simple method used for increasing surface roughness of the materials, allowing also the synthesis of ceramic like oxide films on some metals [50].

Figure 11.4 General concepts of titanium implant modification [Journal of Prosthodontic Research 59 (2015)].

Size-dependent biological reaction at the interface

Cellular level Atomic level

Micro topography Nano structure

Na+

Na+

PO43–

PO43–

Ca2+Ca2+

Macroscopicdesign

Exeter

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• Anodic oxidation is a traditional surface modification method of Ti and its alloys that allows the obtaining of the desired properties of oxide layer by controlling the electro-chemical parameters, such as electrolyte composition and concentration, applied potential or current, temperature, and so on [26].

In addition to the previous fabrication methods, there are many other ways [51–53] that used to nanostructure form and because this area does not allow for describing all the previous methods and others in this book chapter, herein we focus only on the electrochemical anodization method.

11.2.5 Electrochemical Anodization Method

Anodic oxidation is an electrochemical method, which can be easily imple-mented to produce uniform and controllable nanostructures on Ti surface [26]. Anodizing was first used on an industrial scale in 1923 to protect the aluminum metal from corrosion. Anodization is a simple and low temper-ature process commonly employed for thickening the oxide film on Ti and its alloys [54, 55]. Anodic oxidation under galvanostatic [56] and potentio-static mode [57, 58] has been used recently to develop the surface of tita-nium oxide film. Titanium oxide films with nanoporous structures are desirable due to their large surface area and high compatibility as clinical implant materials [59]. Depending on the electrolyte, its concentration and the applied potential, anodic oxide films on pure Ti can grow mostly dense or porous, amorphous or crystalline [26]. Such films are frequently used for biomedical surface preparation. They are thought to protect the sub-strate from corrosion and enhance its biocompatibility. Anodizing is con-sidered an electrolytic passivation process used to increase the thickness of the natural oxide layer on the surface of metal parts. Different electro-lytes are used to form anodized oxide films such as inorganic and organic acids [60–64], inorganic salts [61], mixture of inorganic substances [61], or hybrid inorganic and organic solutions [64]. While amorphous TiO2 films are produced from H2SO4 and CH3COOH [61], anatase TiO2 films are produces from H2SO4 and Na2SO4 [61]. Self-organized nanotubes TiO2 can be generated from viscous glycerol or ethylene glycol with addition of ammonium fluoride [64].

Anodizing increases resistance to corrosion and wear and provides better adhesion for paint primers and glues than do bare metal. Anodizing changes the microscopic texture of the surface and changes the crys-tal structure of the metal near the surface. Thick coatings are normally


porous, so a sealing process is often needed to achieve corrosion resistance. The first visual observation of the different between the native and anodic oxide films formed in acid solutions is that the anodized films have the different colors (Table 11.1). Depending on the applied voltage the anod-ized pure Ti surfaces appeared light blue, dark blue, light gold, deep gold, and violet. In accordance with literature studies [65, 66], the change in the color of the oxide film resulting from the change of the applied anodization voltage is attributable to the different thickness of the anodized oxide layers for different voltage. This first observation confirms that the anodization voltage has a big role in changing the properties of the anodized oxide film and this physical color appearance remains generally the same after the electrochemical measurements were done.

11.2.6 Experimental Tools for Surface Characterization

To investigate the morphology and chemical composition of the passive film formed on the surface of pure Ti naturally or after anodization in acid solutions at different anodization, by using several advanced technolo-gies. XPS has been employed to characterize the surface chemistry of tita-nium implants. XPS enables a much smaller sample in depth (<10 nm) so that surface-specific characterization is possible. The elements on the titanium surface generate a characteristic set of peaks in the photoelectron spectrum at kinetic energies determined by the photon energy and their respective binding energies. The presence of peaks at particular energies

Table 11.1 The colors of the oxide films formed at different anodizing volt-ages in two different anodizing solutions [Applied Surface Science 256 (2010) 5849–5855].

Types of oxide filmsColor of oxide film

in H2SO4

Color of oxide film in H3PO4

Native oxide film No color

Anodized oxide film at 10 V Dark blue violet Dark golden yellow

Anodized oxide film at 20 V Violet Violet blue

Anodized oxide film at 30 V Pale blue Pale blue

Anodized oxide film at 40 V Pale blue Pale yellow

Anodized oxide film at 50 V Golden yellow Golden yellow

Anodized oxide film at 60 V Dark golden yellow Pale rose


therefore indicates existing specific elements on the sample surface. On this basis, XPS provides a quantitative analysis of the surface composition, as does energy dispersive X-ray spectroscopy [67, 68]. As well as providing elemental analysis, the intensity of the peaks in XPS is associated with the concentration of the elements on the sample surface. The energy position and peak shape are slightly altered by the chemical state of the emitting atom, and hence XPS can provide information about the chemical bonding of the elements to the surface.

The crystal structure of the titanium oxides has been characterized by thin-film X-ray diffractometry [69]. The titanium samples are mounted on a goniometer and rotated with X-ray bombardment, thereby form-ing a diffraction pattern of the crystal phase of the passive TiO2 film. TiO2 exists in three main phases: rutile, anatase, and brookite, with the predominant form being the rutile and anatase phases with tetragonal structures.

A distinct advantage of AFM is the ease of sample preparation in com-parison with other conventional imaging techniques such as SEM or trans-mission electron microscopy (TEM). However, the field of view for AFM is limited by the traveling distance of the piezo-electric tube [70]. The basic principle of AFM is measurement of the intermolecular force between the silicon cantilever (AFM tip) and the sample surface allowing visualization at the nano–micro scale of titanium surface structures. Recent research into the nanostructure of titanium has focused on nanotopographical analysis by AFM (Figure 11.5) [70].

11.2.7 In-vitro and In-vivo Studies

The performance of biomaterials used for implants should be evaluated by determining their physical, chemical, mechanical, and biological proper-ties. In vitro studies are performed with microorganisms, cells, or biologi-cal molecules outside their normal biological context. Colloquially called “test-tube experiments,” these studies in biology and its subdisciplines have traditionally been done in test tubes, flasks, Petri dishes, etc. and since the onset of molecular biology involve techniques such as the so-called omics. Studies that are conducted using components of an organism that have been isolated from their usual biological surroundings permit a more detailed or more convenient analysis than can be done with whole organ-isms. In contrast, in vivo studies are those conducted in animals including humans, and whole plants. In the following sessions, we will refer to the in-vitro and in-vivo measurements that used to check the efficiency of tita-nium implants.


Figure 11.5 A representative AFM images of nanostructure titania.

Untreated

Fold

cha

nge

of s

urfa

cero

ughn

ess

*p = 0.01

141210

86420

*p = 0.002HA

A1 A2 A3

A4 A5 A6

TiO2

pH 6.5 pH 5(b)

(a)

11.2.7.1 Stability of Titanium Implants

In the physiological environment, biomedical implants are subjected to corrosion, especially to mechanically accelerated electrochemical processes such as stress corrosion, corrosion fatigue, and fretting corrosion. Fretting corrosion, alone or in combination with crevice corrosion, has been identi-fied as one of the most important modes of corrosion of Ti-based implants such as hip, knee, and shoulder replacements [71]. In this context, the failure due to corrosion remains one of the challenging clinical problems, and all the implantable materials should be tested from a corrosion behav-ior point of view, apart from other properties [72]. Moreover, different approaches should be used in order to enhance the corrosion resistance. In order to investigate the corrosion behavior of Ti-based biomaterials, dif-ferent artificial physiological solutions can be used simulating plasmatic


serum (Ringer’s and Hank’s solution) [73], artificial saliva [74], or artificial sweat [75]. The composition of some of these artificial physiological solu-tions is presented in Table 11.2.

There are a number of electrochemical and physical methods analyzed the efficiency of modified nanostructure titanium surface such as elec-trochemical impedance spectroscopy, chronoamperometry, linear sweep voltammetry, surface profiling, and SEM. In this chapter, we analyzed the nanostructure titanium surface modified by anodization process under different conditions by using electrochemical impedance spectroscopy. The aim of the session is to compare the specific preparation conditions regarding their significance and to correlate the results obtained from elec-trochemical impedance spectroscopy and SEM to find fitting domain.

Electrochemical impedance spectroscopy, EIS, is a non-destructive sensitive technique has a desirable effect on the surface which enables the detection of any changes occurring at the electrode/electrolyte interface that reflect the reaction between implant surface and physiological solution, so it is consider a best electrochemical technique for diagnosis and applica-tion purposes. The term impedance Zω refers to the frequency dependant resistance to current flow of a circuit element (resistor, capacitor, inductor, etc.). Impedance assumes an AC current of a specific frequency in Hertz (cycles/s). Where Zω = Eω/Iω , where Eω = frequency- dependent potential and Iω = frequency-dependent current, this law is similar to Ohm’s Law: R = E/I where R = impedance at the limit of zero frequency. Accordingly,

Table 11.2 Chemical composition of simulated physiological solutions.

Concentration of chemical compound (g/l) Hank solution Ringer solution

NaCl 8.0 8.69

KCl 0.4 0.3

CaCl2 – 0.48

CaCl2.2H2O 0.35 –

NaH2PO4.H2O 0.25 –

Na2HPO4.2H2O – –

MgCl2 0.19 –

MgSO4.7H2O 0.06 –

Glucose 1.0 –


the information content of EIS is much higher than DC techniques or single frequency measurements.

For impedance data may be displayed as either a vector or a complex quantity. A vector is defined by the impedance magnitude and the phase angle, while as a complex quantity, Ztotal = Zreal + Zimag (Figure 11.6). Always EIS data may be presented as phase change (Bode plots) or complex plane (Nyquist plane) and are recommended as standard impedance plots (Figure 11.7). The presence of the phase angle, θ, as a sensitive parameter for any surface changes, gives direct information about the electrode sur-face [76, 77]. For Bode plot, the frequency is explicit, while for Nyquist plot, the frequency is obvious in Figure 11.7.

Analyzing of Electrochemical impedance data are occurred by mod-eling, where electrochemical cells can be modeled as a network of pas-sive electrical circuit elements. A network is called an “equivalent circuit.” The EIS response of an equivalent circuit can be calculated and compared

Figure 11.6 Vector and imaginary representation of EIS measurements.

Vector

Real

Complex plane

Imag

inar

y

θ = phase angle

Magnitude

6

5

4

3

2

1

1 2 3 4θ

Figure 11.7 EIS data as bode and Nyquist plots.

–50000

0

50000

1E + 5

2E + 5

3E + 5

4E + 5

5E + 5

1.5E + 5

2.5E + 5

3.5E + 5

4.5E + 5

5.5E + 5

1E+5

100

90

80

70

60

50

40

30

20

10

0

–10

10000

1000

100

–2E + 5 –1E + 5

Nyquist plot foranodized Ti samplesunder dierentconditions

Bode plot foranodized Ti under dierentconditions

–Z″ (

Ω)

Z(Ω

)

–Pha

se (*

)

1E + 5

Z′ (Ω) Frequency (Hz)

1E + 51000010001001010.10.012E + 5 3E + 5 4E + 50


to the actual EIS response of the electrochemical cell. The electrical ele-ments which commonly used to describe the electrochemistry of implant surfaces are resistance and capacitor (Figure 11.8). The electrochemistry of any electrode can be represented as shown in Figure 11.9, where the resistance (R) and capacitor (C) are commonly used to describe titanium implant surfaces.

The frequency response of resistance electrical circuit is Z = R (ohm) at 0° phase shift. And the frequency response of capacitor is Z = –jωC (Farad) at –90° phase shift.

The EIS results of anodized titanium implant surface show that the phase angle has two maxima (Figure 11.7), which means that the electrode/electrolyte interface is controlled by two time constants. In this figure, the splitting in log f vs. θ diagram (Bode plot) means that both the inner com-pact layer and the outer porous layer are contributing to the film growth kinetics [78, 79], and the diameter of the Nyquist plot is increased with the stability of implant in phy siological solution. The experimental impedance data were fitted to the theoretical data according to proposed equivalent circuits. The presence of non-porous structures titanium surface, the sur-face is homogeneous and the impedance behavior in most cases is best fitted by a simple equivalent circuit (Randles circuit) model circuit 1 pre-sented in Figure 11.10) consisting of a parallel combination of capacitor C and resistor R in series with a resistor Rs, representing the solution resis-tance, the electrode impedance, Z is represented by the following equation: Z = Rs + R /1 + (2πRC)α˛ where ˛ is an empirical parameter (0 ≤ α ≤ 1) and

Figure 11.8 Electrical circuit elements.

Resistance Capacitor Inductor

Figure 11.9 Electrochemistry as a circuit (http:// www.Gamry.com).

• Double layer capacitance• Electron transfer resistance• Uncompensated (electrolyte) resistance Randles cell

(simplied)


f is the frequency in Hz. The above relation takes into account the deviation from the ideal capacitor (R–C) behavior in terms of a distribution of time constants due to variations in properties or compositions of the surface film [80]. The best fitting was obtained using the equivalent circuit 2 pre-sented in Figure 11.10, where the porous structure on the surface film was considered. In this equivalent circuit, the Rp–Cp combination was intro-duced to account for the porous film. This model consists of a resistor, Rs, representing the solution resistance in series to two parallel combinations, Rb and Cb representing the resistance and capacitance of the inner barrier film, and then Rp and Cp, representing the outer porous film resistance and capacitance, respectively.

Constant phase element QCPE is a very general electrical capacitor element used to model “imperfect” capacitors that describe the hetero-geneous nanostructures surface. CPE’s normally exhibit an 80–90° phase shift [26]. From the electrical resistance and capacitor circuit elements of both inner and outer layers can deduce the corrosion resistance and stability of the nanostructures titanium implant surfaces after immersion in physiological solution, where the high values indicate the porosity and the high passivity of these layers. As well as, the decrease of capacitance is an indication of layers thickening due to the adsorption of phosphate and calcium ions from the electrolyte solution via porosity of implant surface [26, 81].

11.2.7.2 Mechanical Characterization

The mechanical properties decide the type of material that will be selected for a specific application. An ideal implant material should be biocompatible, with adequate toughness, strength, corrosion, wear, and fracture resistance [82, 83]. According to the American Society for Testing and Materials (ASTM) identified the most important mechani-cal properties that make titanium the “gold standard” material for the

Figure 11.10 Equivalent circuits used for fitting experimental data of titanium implants.

Circuit (1)

C

Rsoln

QCPE

Rct

CRsoln

R2 R1

Q2 Q1

Circuit (2)


fabrication of orthopedic and dental implants, there are Modulus (GPa), Ultimate Tensile Strength (MPa), Yield Strength (MPa), Elongation (%), and Density (g/cc) [84, 85]. Titanium implant with excellent combina-tion of high strength and low modulus closer to bone has to be used for implantation to avoid loosening of implants and higher service period to avoid revision surgery. There are number of studies investigated the effect of alloying element to increase the mechanical properties and enhance osseointegration [10]. On the other hand, there are another studies correlated the effect of nanostructures implant surface with an excellent mechanical properties [86].

11.2.7.3 Antibacterial Activity

One of the most common complications associated with dental and ortho-pedic implants is bacterial infection. Bacterial colonization and biofilm formation on implants materials may lead to acute and chronic inflamma-tion of the underlying bone and the adjacent soft tissues [87]. The biofilm formation of microorganisms can lead to in growth bone, non-union of the fractures, and implant loosening because it is involved in clinical infection and resistance to antibacterial agents [88]. Different factors affect the clini-cal success of implants, such as degree of bacterial colonization surround-ing the implants and osseointegration of bone implants [89]. Antibacterial implant surfaces based on nanoscale modifications of the titanium, and their alloys appear as an attractive strategy for control of bacterial infections, which involves surface antibacterial activity. The major target of research-ers is to prepare novel nanocoatings for titanium implants with antibacte-rial properties [90]. Thicker nanostructures of TiO2 play an important role to enhance the antibacterial activity of implant surface [26, 91]. Recently, biomimetic methods to produce calcium–phosphate, Ca–P coatings are used to improve the biocompatibility of titanium and have attracted con-siderable research attention [21, 92]. The plate-counting method was used to evaluate the antibacterial performance against bacteria according to the order in the next apparent chart (Figure 11.11) [10]. Applications of TiO2 nanostructure as carriers for nanoparticles have successfully been used to deposit biomolecules such as Cu [93], Ag [94], Au [95] to form pas-sive/active coatings promoting the bone healing of the implanted surfaces and preventing microbial infection. In future, nanoparticle–nanotitania structures surfaces are believed to be a multifunctional biomaterial, which combines antibacterial activity and good corrosion resistance in bioenvi-ronmental, and are likely to expand the range of biomedical application of titanium implant.


Figure 11.11 A flow chart of antibacterial activity test.

Preparation of specimens

Specimens was placed in a glass bottle control

0.5 mL of nutrient broth with S. aureus was poured into the bottle

Each 0.1 mL of sample was transferred to 0.9 mL fromphysiological solution

Diluted in a 10-fold series down 10–7

Each 0.1 mL of sample was plated on nutrient agar

Number of CFU was counted

Ratio of CFUs on each specimen to those onthe control was calculated

11.2.7.4 In-vivo and In-vitro Cellular Behavior

Biological response to titanium implants is considered as an interesting point for recent research. Surface treatment has recently been brought to the attention of researchers as a way to improve biocompatibility of the titanium. How these fundamental surface features determine biological activity? is an important question that is just beginning to be answered. With proper control and management, manipulation of surface features may hold the key to developing innovative implant that not only are eas-ily accepted by the human body but can have a subsequent functional effect. The implant’s surface properties, surface chemistry, surface energy, topography, and roughness influence the initial cell response at the cell material interface, ultimately affecting the rate and quality of new tissue formation [96–98]. It is known that osteoblasts cells initially respond in a differential manner to titanium surface roughness. Higher levels of cellular attachment have been found on rough surfaces of titanium with irregular


morphologies [96, 99] in vitro. Similarly, recent studies have shown that alkaline phosphatase species activity is enhanced on rough titanium and its alloy [100, 101]. Other markers of osteoblasts phenotype were also found to increase on rough titanium implant surface (osteocalcin production) [100, 101]. Cells grown on rougher surfaces exhibited increased produc-tion of collagen [100, 102], prostaglandin E2 [100], and transforming growth factor β (Figure 11.12) [102]. Synthesis of extracellular matrix and subsequent mineralization in vitro were both substantially enhanced on rough textured and nanoporous titanium surface [98]. Cells are able to dis-criminate among subtle differences in surface roughness [103]. Osteoblast-like cells can discriminate not only between surfaces of different roughness but also between surfaces with comparable roughness but different topog-raphies [102]. The morphology of the surface is of significant importance. However, factors and mechanisms underlying the response of cells in con-tact with titanium and its alloys are poorly understood.

Generally, the ability of cells to adhere to implant surface is influ-enced, in part, by surface morphology, which is a general term that includes roughness, texture, porosity, and third body (foreign or wear-induced) particulates. Substrate morphology on the order of microme-ters affects cell migration, reproduction, and metabolic rate in-vitro, and tissue adhesion, inflammation, and capsular contraction in-vivo. These topics were reviewed by researcher [100, 104, 105], as they pertain to modifying the response of cultured cells in vitro, and biocompatibility

Figure 11.12 Photomicrographs shows reactive cartilage that is undergoing endochondral ossification after implantation of TiO2/Ti plate for 2 months; cartilage is growing (expanding) toward the left and cartilage with hypertrophying chondrocytes (Cc) (at right), however, the condroblast (Cb) at left (a, ×10 and b, × 40).

(a)

Cb Cc

(b)


in vivo. Titanium surface roughness (Grade 2 cp. Ti disks, degreased, acid pickled, Ra – 10–40 btm, TiO2 thickness – 100–300 A) affects prolif-eration, differentiation, and matrix synthesis of chondrocytes (cartilage cells) and osteoblasts-like cells, and this surface roughness regulation is cell maturation dependent [106]. For osteoblasts-like cells, cytokine and growth factor production in the microenvironment surrounding a biomaterial are also influenced by surface morphology, suggesting that morphology may modulate the activity of the cells interacting with a bio-material, and affect tissue healing and biomaterial success [106]. It has also been shown that topography-induced changes in cell shape can alter matrix metalloproteinase (MMP-2) gene expression [107]. Alteration of molecules involved in tissue degradation, such as MMP-2, may pro-vide an indication of tissue remodeling activities. The amount of bone-like foci formation, mineralization, and orientation depended on the groove geometry of the underlying implant structure. They hypothesized that bone is engineered to a predetermined configuration, which is dic-tated by surface topography. These microscopic results may explain the increased cohesion between bone and grit-blasted implants on a macro-scopic level.

According to the previous sections in this chapter, implant metals still account for a substantial percentage of clinically used materials and, for many applications, represent the only presently viable reconstructive strat-egy. Although much of biomaterials research has gravitated toward more biologically based materials, there are millions of patients who currently have metallic implants, and millions more who will need these devices in the near future. Commensurate with this clinical need, some of the demo-graphics of metallic biomaterials in the USA were summarized. The areas discussed in this chapter are also expected to be important areas of future research in metallic biomaterials. Significant advances in these areas include the following. In the area of metal breakdown, corrosion and wear are now being studied as coupled processes, leading to greater insight into mechanisms of degradation. In the area of biological response, there has been an increased emphasis on cell and molecular biology and the inte-gration of these disciplines with surface science. Recent data suggest that dose-response trends in cytotoxicity, cellular processes, and gene expres-sion are not necessarily commensurate. In the area of surface modification, many tissue engineering strategies, such as protein immobilization, can be effectively used with metals, providing mechanical integrity along with biological recognition. Lastly, in the area of surface analysis under in-vitro and in-vivo studies automated imaging process analysis is required, which will discuss in the following sections.


11.3 Automated Nanostructures Image Analysis-based Morphology

Visual imaging techniques with significant magnification using micro-scopes have an important role in the advance of science for several centuries. Recently, with the use of SEM, fluorescence microscopy and X-ray microscopy, biologists have captured prominently detailed images of fundamental structures either for in-vivo or in-vitro studies. Rapid growth of supplementary powerful and flexible imaging procedures remains crucial. Biotechnology development demands screening and testing of new materials/nanostructures [108], which requires nanostructures image processing and analysis toward automated system.

11.3.1 Nanostructures Morphology and Properties: TiO2

Nanostructures are one of the options of surface bioactivation of titanium implants that are used in medicine. TiO2 has promising applications in the various fields. Recently, TiO2 has been prepared in the form of crystals, powders, thin films, nanotubes, and nanorods. Its nanostructures have dif-ferent shapes such as nanoparticle, nanotube, biofilm, and nanosheet. Over the past few decades, incessant breakthroughs in the TiO2 synthesis and modification have realized new properties and applications with improved performance. Volume recombination and surface recombination arise for larger particles and very fine particles, respectively. Based on the essential characteristics of nanostructure TiO2 particles, approaches including vari-ous selective surface treatments are used to improve the TiO2-nanostrcture efficiency [109]. Shape and the size of the nanostructures are key factors in determining surface-related properties. Thus, material scientists chal-lenge to synthesize nanostructures with controlled size and shape in order to achieve the desired functional properties for the targeted application. Geometric size, shape, inner tube diameter, and thickness information of nanostructures are analyzed and processed using several microscopic technologies. During the nanostructures synthetic process, generated structures can be investigated and analyzed using image processing and analysis techniques to guarantee accurate synthetic.

11.3.2 Image Processing and Analysis

Nanostructure functional properties are highly relying on its surface mor-phology. Thus, microscopic nanostructures surface images’ analysis and


processing can provide accurate measurements of the nanostructure’s morphology to facilitate consistent characterizing of their properties. In image processing, morphological analysis consists of three main process: (i) image segmentation to separate individual objects, (ii) shape inference, which infer the object’s missing contours, and (iii) shape classification to ultimately classify the objects by shape based on their complete contours.

Hence, nanostructures surface morphology analysis offers the neces-sary feedback to control/tune the synthesis process along with the size and shape of the nanoparticles. Extensive analysis and processing for the cap-tured images for the quantitative and qualitative information on various physical properties including shape, size, thickness, density, length, mor-phology, surface texture, and roughness are required. To perform image analysis, microscopic images are to be captured using a SEM, TEM, or an AFM to analyze shape and size.

Although the obtained images by the microscopes are sufficiently good to be analyzed to identify the required nanostructures properties. Nevertheless, they cannot be analyzed in the raw image format, where the images are abstract. Therefore, the raw image has to undergo a pre- processing to obtain correct information [110, 111]. Additionally, manual identification and surface thickness measurements suffer from low accuracy for image processing and time consuming in a single microscopic image. Consequently, developing an automated image and analysis method to pre-process, recognize, and extract features for the nano structures based on their morphology analysis become a must. The block diagram for the main image analysis and processing steps to be applied on then nanostructures/particles microscopic images is demonstrated in Figure 11.13.

The pre-processing step undertakings contrast enhancement, compres-sion, restoration, and background subtraction. The next crucial step after feature extraction and clustering is the image segmentation process. Image segmentation is defined as the process of partitioning an image into a set of non-overlapping meaningful based on the image features, such as the intensity, color, and texture [112, 113]. The segmentation procedures can be categorized into: edge-based segmentation [114], intensity-based seg-mentation thresholding [115], region-based segmentation [116], and mathematical morphology [117]. However, thresholding is considered a convenient and easy approach for segmentation based on the different intensities in the foreground and background regions of an image [118].

The most essential step after segmentation is the image classification, which is one of the machine learning techniques that conducted to cat-egorize objects/data it into definite classes based on set of features [119]. The classification process generally entails training and testing datasets,


which contains different features or traits such that pattern can be recog-nized. Image classification techniques can be categorized into two fore-most groups: supervised classification and unsupervised classification.

The objective of this section in the current chapter is to discuss dif-ferent automated nanostructures/particles image analysis and process-ing techniques for measuring and classifying specific morphologies of nanostructures’ surfaces and individual nanoparticles even in overlapped cases. Nanostructure image processing/analysis is a very significant part of classification process of nanomaterials quality. In order to clarify the segmentation and classification concepts, an application of mathematical morphology on nanostructure image processing is discussed as follows. Feature extraction is an essential step in the image analysis/ processing. Zhu et al. analyzed SEM images of nanostructures using efficient method for 3D nanostructures reconstruction [120]. The 3D nanostructure shape parameters and morphology were obtained by analyzing the contrast of one single top-view SEM. It provided more details of the nanostruc-tures via conducting computational analysis, such as optical simulation,

Figure 11.13 Image processing and analysis processes.

Clustering

Features extraction

Image analysis

Image pre-processing and enhancement

SEM image acquisition and digitization

Segmentaion

Classication

Interpretation Measurements


roughness measurement, and properties optimizing. Figure 11.14 demon-strated well reconstruction of the morphological features of black silicon even the details which cannot be seen clearly in SEM image.

Figure 11.14(a-a′) depicted that the reconstruction structures distribu-tion fits well with that in SEM image, while Figure 11.14(b-b′) compared the structures in side view. Figure 11.14(c-c′) included a comparison of structure details, which illustrated slope changes between nano-structures on the bottom proved. Petrová et al. [117] introduced selected methods of mathematical morphology for nanostructures microscopic

Figure 11.14 Comparison between SEM image and 3D reconstruction result of black silicon from different views: (a-a′) top view; (b-b′) side view; (c-c′) nanocone details [120].

(a)

(b)

(c)

(a¢)

(b¢)

(c¢)


Figure 11.15 Titanium dioxide tubes image segmentation example: (a) original titanium dioxide tubes image, (b) pre-processed (filtered) image, (c) watershed segmented image, and (d) adaptive thresholding for each segment [117].

(a) (b)

(c) (b)

images segmentation. The authors targeted to a separate the inner area of tubes from the background, where this diameter is considered an important factor representing the material quality. A selected cut of tita-nium dioxide tubes image was used as depicted in Figure 11.15(a). A pre-processing step through filtration is used to reduce the noise level in the image to improve the final segmentation process. Figure 11.15(b) illustrated the output of the filtration process carried on the original image in (a). The image is then divided into segments using Watershed transform as indicated by the red lines in Figure 11.15(c). Afterward, due to the existence of different grayscale levels in each part of the image, an adaptive thresholding was carried out inside each Segment, as demon-strated in Figure 11.15(d).

This segmented output can be used for further image processing steps for classification as well as for analysis to measure the inner diameter of


the titanium dioxide tubes. Also, it can be generalized for any required nanostructure segmentation process. Dazzo and Niccum [121] devel-oped computer assisted for microscopic image analysis named (CMEIAS) software, which carried on microbial biofilm microscopic images. The designed software is accurately able to measure: (i) the microbial cell length, (ii) the microbial biovolume body mass, (iii) pattern recogni-tion rules for morphotype classification of diverse microbial using the k-nearest neighbor classifier, (iv) spatial patterns of coaggregation by measuring the local intensity, and (v) object segmentation of complex color images.

For the classification process, CMEIAS k-nearest neighbor rule Morphotype Classifier was used to distinguish between some morpho-types based on shape analysis measurements. The system was able clas-sify the objects into three different shape subcategories: spheroid, ovoid and coccobacillus. The classifier used the extracted features such as the elongation, compactness, maximum curvature, roundness, area/bounding box area, width/length ratio and eight Fourier descriptors for classification. Figure 11.16 depicted a classification of the microbial biofilm assemblages colonized on polystyrene slides using k-nearest neighbor classifier.

11.3.3 Nanostructures/Particles Image Analysis in In-vitro and In-vivo Studies

In the science and medical fields, studies can be categorized into in-vitro and in-vivo studies. The former refers to studies that performed with cells, particles or biological molecules outside their normal biological context. In contrast, the in-vivo studies are those conducted in humans, model animals and plants [122]. Consequently, in the microscopic nanostruc-tures/particles studies, these two studies can be evaluated and requires image analysis and processing, by studying either their synthetic and its

Figure 11.16 Biofilm classification example: (a) original microbial biofilm assemblages colonized on polystyrene slides and (b) output of the morphotype classification of each cell in the biofilm landscapes in (a) [121].

(a) (b)


morphological structure, size and shape (in-vitro study) or by studying the effect of its applications in implantation on the cells tissues or bones of the living humans or animal models (in-vivo).

Numerous areas of chemistry, bio-chemistry, medicine, technology, and physics utilized microscopic image analysis. Typically, the input image may have resolution of 1024 × 1024 pixels, while the objects of interest (e.g., nanostructures, nanoparticles, cells, tissues) have much less number of pixels. Consequently, identification, counting, and classification become complex due to the objects of interest strong variability, poor resolution, objects overlapping, and noisy background. These drawbacks required enhancement in the image processing and analysis systems to be able to handle the variability within each class as well as the variability between the classes.

Additionally, the development of new materials for biological applica-tion such as implantation requires in-vitro testing of cell/surface interac-tions. Cell adhesion and spreading are complicated to enumerate as most materials are non-transparent and transmission microscopy cannot be used. Contrast in reflection microscopy is rather poor. Therefore, extensive studies are conducted to discover automated algorithms and image analy-sis/processing tools to accurately detect, count, and classify nanostructures as well as to assessment their synthetic process.

Several in-vitro studies have been conducted on the nanostructures/ particles. Fisker et al. (2000) proposed an active contour technique to estimate particle size distribution [123]. The nanoparticles’ shape was assumed to be an ellipsis in the algorithm of separating overlapping particles. Korzenowski [124] developed an automated technique to per-form segmentation, inference, and classification of partially overlapped nanoparticles (UECS).

Gontard et al. [125] applied a local thresholding technique to identify particles based on the spatial varying intensities. Ravindran et al. described an image processing system to estimate the orientation correlation func-tion from the SEM the chemically patterned nanoscale structures. The orientation correlation function is considered a measure of the spatial range over which nanoscale structures maintain their structural similarity [126]. Chen and Ho (2008) employed a suggested method to estimate the size distribution of spherical nanoparticles by sequentially conducting the Laplacian edge detection algorithm and a radius estimation algorithm [127]. Glotov [128] proposed a circular decomposition algorithm to analyze very complicated chain-structured nanoparticles aggregates. Alexander et al. proposed a new quality-control scheme for the nanopore structure of the mesoporous silicon particles [129]. The authors developed


image analysis software to automatically inspect the SEM images in a fully automated approach. The proposed algorithm initially identified the pre-cise position and shape of each nanopore. Afterward, it performed texture segmentation by generating a graphic display of the detected nanopores and of their boundaries. The segmentation process output was considered a key quality-control requirement is fast computing speed. In addition, the designed software computed the shape characteristics of individual nanopores, for instance area, outer diameter and the eccentricity. The means, standard deviations, and histograms of each pore-shape feature were calculated for analysis and the outliers’ detection among families of nanoparticles.

Valmianski et al. designed a software package called Microscopy Image Segmentation Tool (MIST) for analysis of microscopy images, which enclose large collections of small regions of interest (ROIs) [130]. Formerly, this software was developed for analysis of porous anodic alumina SEM images. Additionally, its potential has been expanded to analyze nano- and mesoscopic structures as well as biological tissue and inorganic/organic film grain structure. MIST affords a robust segmentation algorithm for the ROIs. Hughes et al. designed open-source software to fully characterize an ensemble of SEM images [131]. The authors used artificial SEM images to impartially compare the classical image processing methods through each stage of the proposed workflow: acquisition, preprocessing, segmentation, labeling, and object classification. The classification process was performed using semi-supervised machine learning technique to decompose the nanoparticles image into particle subtypes.

Along with the previously mentioned in-vitro studies, researches are concerned with in-vivo studies to investigate the effect of using such syn-thetic nanoparticles/structures when used for implantation on the cells, tissues, or bones of the living bodies. Sungkawor et al. (2007) investigated from the morphological, the cytotoxic effects of TiO2-nanoparticles in the dark on the growth of human cervical carcinoma cell colonies using cap-tured images by the SEM [132]. The in-vitro and in-vivo studies along with the image processing technique/fractal analysis were performed. The fractal geometry is an approach of quantifying objects with a complex geometri-cal structure that is difficult to quantify using regular Euclidean geometri-cal methods. The in-vivo studies proved that these colonies were abnormal in shape and size. Moreover, the size of the control colonies appeared to be larger than those of the treated group with TiO2-nanoparticles. The experi-mental results indicated that fractal dimension can provide useful feature by itself or in conjunction with other shape features for the classification of cancer colonies.


11.3.4 Orthopedic and Dental Applications Using Titanium Surfaces and Its Alloys Based on Digital SEM Imaging Analysis

The titanium superficial characteristics influence the formation of the implant/bone interface. Digital image processing was used by Diniz et al. to quantify and to create parameters on the surface of titanium blasted using particles of aluminum trioxide Al2O3 [133]. Three digital images from each sample surface were captured using SEM. In addition, parameters related to the alumina phase, such as the concentration level, the area, and peri meter of the particles and their circular form factor were measured. The presence of residual aluminum particles may have deleterious effects on the forma-tion of the osseous/implant tissue. Thus, after performing segmentation the area, perimeter, and circular form of the Al2O3 particles was calculated digitally using image processing software. Figure 11.17 demonstrated the surface variation brightness, which enhanced by color mask. The image variation indicated a difference in the intensity of its pixels. The bright-ness problem was corrected mathematically during an intermediary step in the image processing operations. Afterward, the mean area, perimeter, and circular form of the Al2O3 particles were calculated digitally.

Consequently, digital image processing allows accurate quantifi-cation and parameters creation related to the presence of the Al2O3 phase. An effective and appropriate process to characterize biomaterial- manufacturing processes can be performed using automated image pro-cessing approaches. Games et al. [134] superficially modified commercially pure titanium sheets used in dontology and orthopedics. The authors’ objective was to promote bioactivity by generating titanium oxides doped with phos phorous on the surface. The characterization and quantification of the generated deposits were considered a starting point for the future

Figure 11.17 (a) Represented the variation in the image is based on the brightness difference and (b) represented the original image after applying image processing and analysis and analyzed [133].

(a) (b)


application of these materials. The practical characterization methods for evaluating the chemical and phase composition on the modified surface were X-ray diffraction and micro-Raman spectroscopy analysis. As well as segmentation of SEM images was conducted using image analysis tech-niques. Image segmentation through the use of mathematical morphology operators was performed for subsequent measurement and quantifica-tion of generated oxides by the anodization process on anodized titanium. Figures 11.18(a) and 11.19(a) demonstrated two samples of rough surface SEM microscopy images at magnifications of 100× and 500×, respectively. The outputs of the mathematical image analysis were illustrated in (b) the binary images and (c) the superposition of both images in (a) and (b), in both Figures 11.18 and 11.19.

The results provided that the percentage of oxides at 100× magnifica-tion was 24% and at 500× magnification was 42%. The calculated values depicted a high variation since Figure 11.19 was taken in a denser particle zone of the sample. Thus, Figure 11.18 can be considered the more repre-sentative one since it took larger zone of the sample. Consequently, math-ematical image analysis is a dominant tool that can be applied to various images with similar contrast and color.

Generally, from the preceding studies, it is clear that characterization of a large ensemble of nanostructures in a set of images is a persistent

Figure 11.18 (a) SEM magnified image by 100×, (b) binary images, and (c) superposition of both images in (a) and (b) [134].

(a) (b) (c)

Figure 11.19 (a) SEM magnified image by 500×, (b) binary images, and (c) superposition of both images in (a) and (b) [134].

(a) (b) (c)


challenge in materials science. Advances in image analysis and processing have enabled the automatic counting and sizing of particles various appli-cation. The foregoing widespread literature review established that various analyses and processing techniques can be involved for SEM nano particles/structures image for identification, segmentation, and classification for broad range of applications as in-vitro studies. As well as it is important to control the size, shape and surface of the synthetic nanoparticles/ structures based on the application under concern. Generally, image processing tech-niques are desirable to extract decisive information pertinent to nano material characterization. Manual analysis methods are slow, expensive, and labor intensive. Therefore, there is a strong requirement to develop fast and reliable techniques either for in-vivo or in-vitro studies to achieve process control compatible with automated processing of nanostructures images.

Recent discoveries in nanotechnology demonstrated that morphology and distribution of the nanostructures are the key aspects of their proper-ties. Proper and adequate classification of the nanostructures is considered a problem of nanometrology. The challenge is to develop novel measure-ment standards/techniques in the view of future technological advances. As a future aspect, it is recommended to develop automated image analysis and processing algorithms to support the digital SEM imaging of the advanced titanium surfaces and its alloys for orthopedic and den-tal applications in terms of in-vivo and in-vitro studies. The thickness of the nanostructures can be measured using image processing algorithms. Additionally, image processing classification process can be conducted to classify the different shapes of the nanostructures. Image processing and analysis are considered promising field that can support the nanostruc-tures and nanoparticles assessment during their synthesis steps.

11.4 Conclusion

Biomaterials domain is highly encompasses and multidisciplinary the pro-gression, evaluation, and use of materials to regenerate or replace tissues/organs. Development and study of titanium surfaces for orthopedic and dental implant applications is derived for soft tissue implants and medical device applications. Nowadays, research focuses on biomimetic materials and the study of its performance and interaction with biological systems in in-vivo and in-vitro models. A variety of nanostructures implants sur-faces are being exploited to improve the bone growth at the implant–tissue interface.


As implant material, titanium is used widely in the surgery field. Natural or artificial compact formation and protective oxide are convenient tools for metal protection and a superior approach to generate phosphate depos-its to improve biocompatibility and bone fixation with the existing tissue. Breadth of interrelated domain knowledge of both the high-resolution microscopy supported and image processing/analysis lead to progress in nanotechnology applications. Image processing techniques enabled auto-mated nanosystem characterization at scales ranging from the individual particle to the entire composite and nanostructures. Preprocessing, feature extraction, segmentation, and object classification techniques for nano-structure images are significant to streamline nanostructure characteriza-tion. Test images can be utilized to assess a variety of image processing and analysis segmentation techniques, ranging from thresholding to super-vised learning. Mathematical morphology is a theory based on concepts of algebra, geometry, topology, and set theory that used in image process-ing to characterize physical and structural properties of different materials. It has been considered a successful technique in image segmentation, as it examine if the geometric structures of an image overlap with a small localized structuring element (pattern). Even with the presence of noise as well as low contrast level, significant characterizing of the material can be achieved using image processing techniques. Characterizing the tita-nium surface is vital in the material manufacturing process evaluation as it affects the implant tissue. Titanium implant surfaces and its alloys for orthopedic and dental applications can be supported by digital SEM imag-ing analysis and processing both in in-vitro and in-vivo studies.

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Editor name: Yoon S. Lee

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Ref. [43] is Vol 58, No. 3 [43]Olszta, M. J., Cheng, X., Jee, S. S., Kumar, R., Kim, Y. Y., Kaufman, M. J., ... & Gower, L. B. (2007). Bone structure and formation: a new perspective. Materials Science and Engineering: R: Reports, 58(3), 77-116.


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Please provide editor names for Ref. [129].

viip

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Regarding ref. 129: Editors are: Xiaoyi Jiang & Nicolai Petkov

revised-11_tiwari_ch011_449-492

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