automatictissueimagesegmentationbasedonimageprocessing...

Research ArticleAutomatic Tissue Image SegmentationBased on ImageProcessingand Deep Learning

Zhenglun Kong 1 Ting Li 2 Junyi Luo3 and Shengpu Xu 2

1Northeastern University Boston MA USA2Institute of Biomedical Engineering Chinese Academy of Medical Science and Peking Union Tianjin 300192 China3University of Electronic Science and Technology of China Chengdu China

Correspondence should be addressed to Shengpu Xu xushengpu163com

Received 4 May 2018 Revised 17 September 2018 Accepted 20 December 2018 Published 31 January 2019

Academic Editor Cesare Valenti

Copyright copy 2019 Zhenglun Kong et al is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Image segmentation plays an important role in multimodality imaging especially in fusion structural images offered by CT MRIwith functional images collected by optical technologies or other novel imaging technologies In addition image segmentationalso provides detailed structural description for quantitative visualization of treating light distribution in the human body whenincorporated with 3D light transport simulation methods Here we first use some preprocessing methods such as waveletdenoising to extract the accurate contours of different tissues such as skull cerebrospinal fluid (CSF) grey matter (GM) and whitematter (WM) on 5 MRI head image datasets We then realize automatic image segmentation with deep learning by usingconvolutional neural network We also introduce parallel computing Such approaches greatly reduced the processing timecompared to manual and semiautomatic segmentation and are of great importance in improving the speed and accuracy as moreand more samples are being learned e segmented data of grey and white matter are counted by computer in volume whichindicates the potential of this segmentation technology in diagnosing cerebral atrophy quantitatively We demonstrate the greatpotential of such image processing and deep learning-combined automatic tissue image segmentation in neurology medicine

1 Introduction

Nuclear magnetic resonance imaging gives a clear and high-resolution image of brain tissues [1] It is a common methodfor clinical examination of brain diseases e human brainstructure is very complicated Important tissues include greymatter white matter and cerebrospinal fluid [2](Figure 1(a)) ese tissues play a key role in memorycognition awareness and language [3] Cerebral atrophyexpansion [4 5] and leukodystrophy [6] are serious braindysfunction diseases that have a high incidence in infantsand elderly people [7] However crucial tissues such ascerebrospinal fluid grey matter and white matter are hardto differentiate due to blurry boundaries especially in thecross-sectional images that do not show the center of thebrain as shown in (Figure 1(b)) As a result it is hard fordoctors to analyze them separately and find the location ofthe disease [8] With the popularization of image-aided

medical diagnosis computer-aided doctors can improvethe efficiency of segmenting [9] the grey matter and whitematter of the brain MRI In MR imaging different signalintensities and weighted images (T1 weighted and T2weighted) canmake the image display at different grey levelsSince the T1 brain magnetic image shows that the soft tissueis better [10] the experiment selects the brain magneticresonance T1-W image as the experimental sample

Many approaches have been made to segment the brainimage automatically Segmentation algorithms based onregional texture and histogram thresholds [11 12] aresimple but lack accuracy reshold is a simple but effectiveway to segment images However there are some limitsregarding only using this method for segmentation First thegrey scale of tissues may not be restricted in one range ismeans that if we simply use threshold to locate the tissues itmay fail to separate all the parts Secondly the thresholdusually does not consider the spatial properties of an image

HindawiJournal of Healthcare EngineeringVolume 2019 Article ID 2912458 10 pageshttpsdoiorg10115520192912458

For example the skull is a round structure that covers theother tissues is can help us to determine the location oftissues and get more accurate segmentation images As aresult threshold determination is often considered as anearly stage sequential image process Later methods relatedto fuzzy c-means (FCM) [13 14] and machine learning areintroduced e atlas-based method is also widely used forbrain image segmentation [15] It has a relatively completesystem framework However explicit information such asintensity and spatial features is required in order to getaccurate results [16] Spatial and intensity features could beavoided by using convolutional neural networks (CNNs)[16] Convolution neural network proposed by LeCun et al[17] is a deep supervised learning method [18] It has beenapplied in many fields and has made great success in imagerecognition [19 20] speech recognition [21 22] naturallanguage processing and so on CNNs obtain the convo-lution weight by means of cyclic convolution and sampleswith a supervised training mode e final realization isdirectly extracted from the original input which is con-ducive to the classification featurese features in the imagerecognition are texture shape and structure

MICCAI is a conference held every year focusing onmedical image computing and computer-assisted in-tervention [23] Recently many methods related to deeplearning were presented in the conference Zhang et alpresented a 2D patch-wise convolutional neural networks(CNNs) approach to segment tissues from multimodal MRimages of infants [24] An NlowastN size picture block wasextracted from a given image and the model is trained withthese blocks en the label was given to the correctidentification class In order to improve the performance ofblock training framework multiscale CNNs used a variety ofways with different patch sizes e outputs of these ap-proaches were combined with the neural network and themodel was trained to give the correct label is method inthis paper did not include a pooling layer or consider the

relation between the patches Yang et al [25] used a deepactive learning framework to reduce the annotation effort Itwas combined with fully convolutional network and activelearning Man et al [26] proposed combining MRI multi-modal information to extend CNNs to 3D which wascomposed of multiple modes that formed 3D raw data

In our paper we use image enhancement operators andmorphometry methods to extract the accurate contours ofdifferent tissues on 5MRI head image datasets After that weutilize convolutional neural network to realize automaticsegmentation of images with deep learning Such approachesgreatly reduced the processing time compared to the othermethods We also introduce parallel computing to furtherspeed up the processing speed Our work has a great po-tential in the medical field for diagnosing brain disease

e rest of the paper is organized as follows In Section 2we describe our dataset model and training method Ourexperiments and comparison with other methods are dis-cussed in Section 3 Section 4 concludes the paper

2 Materials and Methods

21 Dataset Our dataset includes 5 patientrsquos brain MRI T1-W images For every patient we have 160 images with a totalof 800 images e size of the images is 256 times 256 pixelsEvery pixel value in the matrix is an integer between 0 and255 Figure 2 shows some typical MRI of a human braineMRI data used to support the findings of this study areavailable from the corresponding author upon request

22 Preprocessing Due to the complicity of the brainstructure there exist many overlapping regions in each MRimage Image preprocessing can improve both the efficiencyof the algorithm and the reliability of the segmentationresults Image noise reduction [27] and enhancement canmake the image more conforming for viewing By removing

GM CSF

WM

(a)

Skull

(b)

Figure 1 MRI images (a) location of the 3 tissues WM is the area where the color is light GM is the gray boundary around theWM CSF isthe black parts inside the skull We focus on segmenting the tissues in the red circle (b) Cross section of a side of the brain the tissues arehard to distinguish by eyes

2 Journal of Healthcare Engineering

the bright skull we can avoid it from affecting the accuracyof the segmentation of the brain Image noise reductiondirectly affects the result of segmentation

Wavelet domain denoising is used to transform noisysignals from time domain to wavelet domain [10] by usingmultiscale transformation We removed the wavelet co-efficients of noise from all scales to obtain the wavelet co-efficients of signals Finally the signals are reconstructed by awavelet transforme image after noise reduction preservesthe details of the original image and the visual effect be-comes clearer e histogram equalization method is used toenhance the image of the cerebrospinal fluid grey matterand white matter

We collected the grey level of all 800 MRI images andgenerated a histogram that contains all the points as shownin Figure 3 e result shows 4 peaks each stands for a kindof tissue [28] e background grey value is not shown in thefigure which is smaller than 35 From the histogram we canremove pixels that are not in the grey level range of the GMWM CSF and skull We convert them to level 0 to reducethe noise e histogram shows four thresholds whichstands for the four tissues It seems that we can segment theimage by only using this result However there are somelimitsreshold usually does not consider spatial propertiesof an image For example the shape of the skull is round andlocated around the other tissues Also the grey level of atissue may not be restricted around one region e greylevel of GM may be in the CSF region depending on thelocation us the result may not be accurate by only usingthe threshold as an analysis measuring method

23 Convolutional Neural Network Convolutional neuralnetworks (CNNs) have recently enjoyed a great success inimage recognition and segmentation e basic structure ofCNNs consists of two layers One is the feature extractionlayer (C1 C3) e input of each neuron is connected to thelocal receptive domain of the previous layer extracting thelocal feature Once the local feature is extracted its posi-tional relationship between the others can be determinede other layer is the featuremapping network layer (S2 S4)Each computing layer is composed of multiple feature maps[29] e feature map is a flat plane all neuron weights areequal e feature mapping structure uses the sigmoid

function [30] as the activation function of the convolutionnetwork In addition the number of free parameters of thenetwork is reduced because of the weights shared by aneuron on a mapping surface Each convolutional layer inthe CNN closely follows a computing layer for local averageand second extraction is unique extraction structurereduces the feature resolution

e C1 layer (Figure 4) is a convolutional layer with sixfeature maps Each neuron in the feature map is connectedto the 5lowast 5 input e size of the feature map is 28lowast 28 S2 isa pooling layer with six 14lowast14 features Each unit in thefeature map is connected to the 2lowast 2 neighborhood of thecorresponding feature map in the C1 e four inputs ineach unit are added in S2 and multiplied by a trainableparameter along with a trainable offset e 2lowast 2 receptivefield of each unit does not overlap so the size of each featuremap in S2 is 14 of the size as in C1

e C3 layer is also a convolutional layer which uses akernel of 5 times 5 to convolute the layer S2e feature map hasonly 10 times 10 neurons but with 16 different convolutionkernels Hence there are 16 feature maps Each map in C3consists of all 6 or several feature maps in S2e reason whywe do not connect each feature map of the S2 to C3 is thatthe incomplete connection mechanisms keep the number ofconnections within a reasonable range Moreover it destroysthe symmetry of the network Since different feature mapshave different inputs it forces them to extract differentfeatures e S4 layer is a pooling layer that consists ofsixteen 5lowast 5 size feature maps Each unit in the feature mapis connected to the 2lowast 2 neighborhood of the correspondingfeature map in the C3 same as the C1 and S2 e F6 layerhas 84 units and is fully connected to the C5 layer Finallythe output layer is composed of a Euclidean radial basisfunction unit each of which has a unit with 84 inputs

e output of the convolution layer is the sum of theconvolution kernel and the output of the upper layer

xlj f 1113944

iisinMj

xlminus1i lowast k

lij + b

lj

⎛⎜⎝ ⎞⎟⎠ (1)

where l is the layer number k is the convolution kernel b isthe bias x is the input and Mj is the chosen feature map

e number of input and output in the pooling layer arethe same while the dimension of the maps is reduced

Figure 2 Samples of a patientrsquos brain cross-sectional image

Journal of Healthcare Engineering 3

xlj f βlj down xlminus1i( ) + blj( ) (2)

e parameters of the CNN are set as follows (Figure 5)e neural network is divided into three layers the inputlayer is many 4lowast 4 pixel units the second layer is theconvolution layer with 6 kernel functions of 3lowast 3 and thethird layer is the down-sampling layer with six 2lowast 2 pixelunits Finally the parameters of the stable network aftertraining are obtained In each layer there are many 2Dplanar elements and each 2D planar element has manyindependent neurons e output has six pixels thusextracting the deep feature data We used Adam algorithmfor the learning method We set the initial learning rate to0001 and the momentum to 05 We used the cross entropyas the loss function Our CNN was trained for 50 epochseach consisting of 20 subepochs Our batch size is 5Training samples are randomly selected from the total 800images 600 images are for training 100 images are forvalidation and 100 images are for testing

24 Parallel Computing MPI is at the core of manysupercomputing software frameworks [31] We used a Caeframework that adopts the MPI eMPI enables the clusterversion to optimize the data parallel to Cae It supportscommand line Python andMATLAB interfaces and variousprogramming methods e CPU information is Intel(R)Xeon(R) CPU E5-2670 0 260GHz We convert the 800matrices into one large matrix which has the size800lowast 65536 In addition we added a row for placing theimage number Each row is an image e rst number ineach row is the index of the image e image data are puttogether in one matrix us the processing involves all theimages at one time We adopt the master-slave mode [32] Itincludes two sets of processes the master processor is incharge of processing the work orders [33]e slaves executethe work that the master processor assigns

In our work one node acts as the master node which isresponsible for data partition and allocation e othernodes complete the calculation of local data and return the

45 times104

4

35

3

25

2

15

1

05

040 50 60 70 80

Grey level

Pixe

ls

90 100 110 120 130

Figure 3ere are four peaks in the histogram From left to right the peaks stand for the following cerebrospinal uid (40ndash57) greymatter(61ndash798) white matter (86ndash110) and skull (110ndash130)

Inputs32 times 32

C1 featuremaps

628 times 28

S2 featuremaps

614 times 14

C3 featuremaps

1610 times 10

S4 featuremaps

165 times 5C5 layer

120F6 layer

84Outputs

10

Fullyconnected

Fullyconnected

FlattenMax-pooling2 times 2 kernel

Max-pooling2 times 2 kernel

Convolution5 times 5 kernel


Figure 4 General structure of CNN e input layer is 32lowast 32 e input is convoluted to six feature maps in the C1 layer by 5lowast 5convolution kernel S2 is a pooling layer with six 14lowast14 features Each unit in the feature map is connected to the 2lowast 2 neighborhood of thecorresponding feature map in the C1 layer e C3 layer is also a convolutional layer that uses a kernel of 5 times 5 to convolute the layer S2eS4 layer is a pooling layer that consists of sixteen 5lowast 5 size feature maps e C5 layer is a convolutional layer with 120 feature maps e F6layer has 84 units and is fully connected to the C5 layer e output layer has a unit with 84 inputs


result to the master node As shown in Figure 6 the masternode rst reads the data and assigns them to the other nodesand then selects the center of each cluster e slaves cal-culate the distance from each point to the center of the datablock then mark the clustering of each point calculate thesum of the distances between all the points of each cluster tothe center of the cluster and nally return these results to themaster e cluster centers stand for the tissues in the GMWM CSF and skull which are spatial coordinates We usethe Euclidean distance to nd the center of the tissue featureclusters respectively and set the parameter z 05 whichtakes out 50 feature points that are nearest to the featurecenter points to accurately characterize the quantitativecharacteristics of the dierent types of dataemaster nodewill calculate the new center point send to other processesand calculate the other process from the clustering of allpoints to the center of the sum of the distance e processwill continue until the sum of the distances of all the clustersis constant

3 Results

31 Tissue Segmentation Our work shows some satisfyingresults (Figure 7(b)) e images in the left column are theoriginal images ey show a full MRI image of the tissues ofthe brain and other parts such as the facial skull muscle andears After removing the other parts of the head apart from

the brain that are considered noise we successfully segmentone brain image into 4 images In each image we set the grayvalue of the tissue as 255 and the background as 0

e result of the skull shows a curved shape located onthe frontier of the brain e cerebrospinal uid is thesegment between the skull and the grey matterwhite mattere grey matter and white matter are also accuratelysegmented

To test the esectciency of our method we also did seg-mentation without preprocessing As shown in Figure 7(a)the results include a large amount of noise including partsthat are not brain such as the nose eyes and other facialstructures

32 Comparison with Visible Chinese Human (VCH) Ourresult was compared pixel by pixel with the images seg-mented by an expert operator To quantize our result wecalculated the percentage of each tissue in the human brainand then compared it to the visible Chinese human head(VCH) model [34] e VCH model is a very good pre-sentation for the anatomical structure It is mostly used inmodeling light propagation [35] It can help calculate thevolume of brain tissues because the intensity of light changeswhile propagating e VCHmodel is developed from high-resolution cryosectional color photographs of a referenceadult male [36 37] It includes various types of tissues from a

Input 4 lowast 4 Layer 2 lowast 2 Layer 1 lowast 1

Layer 3 lowast 3

Output

Figure 5 Our CNN structure

Read the MRI data from the file (800 lowast 65536 matrix)

Divide the dataset into 1nand send to the slave nodes

Randomly set a center foreach tissue

Calculate the distance from each point to the tissue center

Mark the clustering of each point

For each cluster calculate the sum of the distances between

each point and the center

Master node

Slave node

Send data to slave node


Iterate untilsum converges

Figure 6 Parallel computing master-slave method e master processor is in charge of processing the work orders e slaves execute thework that the master processor assigns e process repeats until the sum of the distances of all the clusters is constant


standing frozen man body e section precision is hori-zontally 002 cm interval and the digital color image has aresolution of 001 cm per pixel which is higher than CT andMRI [38] us it is one of the most realistic head modelsthat contains precise cerebral cortex folding geometry [39]

e data are shown in Table 1 Overall our result is quitesatisfying We manage to locate the boundary and segmentthe tissues accurately (Figure 8(a)) We then calculated theratio between the grey matter and white matter and com-pared it to the VCH result giving an accuracy of 95

(Figure 8(b)) is can be used in diagnosing disease such ascerebral atrophy which is caused by grey matter or whitematter reduction ere are still some deviations betweenour result and the ground truth is is caused by theremaining noise and the insusectciency of the algorithm eMRI dataset contains slit images of the brain instead ofcrosscut which shows a full head rather than only the brainAs a result more noise will be generated from the other partof the head Taking the GM percentage for example someparts that are apart from the brain have the same grey value

Original image Skull Cerebrospinal fluid Grey matter White matter

With

out p

repr

oces

sing

(a)


With

pre

proc

essin

g

(b)

Figure 7 (a) Images generated without preprocessing e rst column shows the skull results the second column shows the cerebrospinaluid results the third column shows the grey matter results the fourth column shows the white matter results (b) Images generated withpreprocessing


as the GM which adds to the total percentage of the GM Forfurther research we will use the dataset from BrainWeb anonline interface that provides a 3D MRI-simulated braindatabase [40] It also provides a fuzzy model for users toestimate the partial volume and is a good way to verify theaccuracy of our method

For the ratio between the grey matter and whitematter we compared our result with the VCH modelalong with some studies done by others Bartlett et alintroduced an interactive segmentation (IS) method to getthe volume of the GM and WM from MRI images [41] Geet al investigated the eects of age and sex on the GM andWM volumes by using volumetric MR imaging in healthyadults [42] e average results are also shown in Table 2

We also calculated the Jaccard index for each tissue asshown in Table 3

Our ground truth is the visible Chinese human Wecalculated the coesectcient between our result and the groundtruth Our method performs well on segmenting CF GMand WM outperforming the methods from Hasanzadehet al [44] and Luo et al [26]

33 Comparisonwith FreeSurfer For further experiment wecompared our result with FreeSurfer FreeSurfer is thesoftware built for analyzing and visualizing the structuraland functional neuroimaging data from cross-sectional orlongitudinal studies [43] e results are shown in Figure 9Our method has many advantages FreeSurfer can onlylocate the WM and GM from an MRI image Also it cannotshow separate results except theWMe image of theWMhas many defects it has many miss-labeling and error-labeling such as white spots in the image Our methodperforms better than this software we can segment everytissue clearly and also display the results separately

34 Runtime By introducing parallel computing wemanaged to reduce the runtime We adopt the master-slavemode One node acts as the master node which is re-sponsible for data partition and allocation e other nodescomplete the calculation of local data and return the result tothe master node is is important when facing large dataOur result is not signicant due to the limitation of the datasize e larger the dataset the better the result obtainedFigure 10 shows a visualized result of the tendency of theruntime

4 Discussion and Conclusion

We used image enhancement operators and morphometrymethods to extract the accurate contours of four tissues theskull cerebrospinal uid (CSF) grey matter (GM) andwhite matter (WM) on 5 MRI head image datasets en werealize automatic image segmentation with deep learning byusing the convolutional neural network Approaches such asregional texture and histogram threshold algorithms andfuzzy c-means (FCM) have limitations in processing timeaccuracy and datasets [16] In our paper the percentage ofeach tissue is calculated which can be used as a criterionwhen diagnosing diseases such as cerebral atrophy oftencaused by the grey matter or white matter reduction [8] Wealso used parallel computing to reduce the runtime

In our method the preprocessing step improved theesectciency of the algorithm and the reliability of the seg-mentation result Wavelet domain denoising is used totransform noisy signal from time domain to wavelet domain

Table 2 e ratio between the grey matter and white matter

Type GMWMMRI 212VCH 222IS 203Ge 15

Table 3 Jaccard index of four tissues

Type Jaccard indexCSF 09431GM 09020WM 09142Skull 08799

Table 1 A comparison of the percentage of each tissue in the brain

Type VCH () MRI ()CSF 3765 3854 plusmn 221GM 2708 2733 plusmn 147WM 1235 1295 plusmn 094Skull 1281 1132 plusmn 133

5000

4000

3000

2000

1000

000CF GM WM

Tissue

Perc

enta

ge

Skull

VCHMRI

(a)

25

2

15

1

05

0VCH

Ratio

of G

MW

M

Our result

222 212

(b)

Figure 8 (a) A comparison of the percentage of each tissue in thebrain between our result and the VCH result (b) Ratio of greymatter and white matter


[10] by using multiscale transformation We removed thewavelet coesectcients of noise from all scales to obtain thewavelet coesectcients of signals is way the esectciency of thealgorithm and the reliability of the segmentation result wereimproved

We introduced convolutional neural networks to ourworke CNN consists of two types of layers a convolutionlayer and a pooling layer Multiple feature maps are gen-erated from the convolution layer after convolution Afterthat the pixels of each group in the feature map are modiedby adding weighted values and oset along with a sigmoidfunction to get the feature map in the pooling layer Withmultiple convolution and pooling layers we were able to getmore accurate results in less amount of time compared tomanual and semiautomatic segmentation We also usedparallel computing to further reduce the runtime of theprocess We adopted the master-slave mode by setting onenode as the master node which is responsible for datapartition and allocation along with other nodes to completethe calculation of local data and return the result to themaster node

We compared our results with the data from the visibleChinese human (VCH) head model [34] e VCH modelgives a very good presentation of the anatomical structureand is mainly used in modeling light propagation [35] edata are collected from high-resolution cryosectional colorphotographs of a reference adult male It includes varioustypes of tissues from a standing frozen man body whichincludes precise cerebral cortex folding geometry [37] Itgave us an average percentage of each tissue (skull cere-brospinal uid grey matter and white matter) in the braine deviation of our result was less than 221 Anotherimportant index is the ratio of GM to WM which helps inevaluating certain pathological changes of brain Our resultis also very satisfying with an accuracy rate up to 95 Ourdataset includes 5 human brains with 160 images eacherefore the results are convincing Our work concentrateson the total percentage between the tissues we did notcompare the accuracy of our boundary with other studiesFor further research we will focus more on the comparisonbetween the boundary and use dataset from BrainWebwhich is an online interface that provides 3DMRI-simulated

Original imageLabeled

(FreeSurfer) White matter(FreeSurfer) White matter (our study) Grey matter (our study)

Figure 9 Comparison between our method and FreeSurfer

2635

2041

16581496 1428

1 2 3 5 9Process number

Runt

ime (

s)

300

250

200

150

100

Figure 10 Runtime under dierent numbers of processes


brain database It provides a fuzzy model for users to es-timate the partial volume and is a good way to verify theaccuracy of our method

Our research has some limitations First of all due tolimited time our dataset is not very large We will increasethe quantity and also the variety of the samples includingdifferent races such as black white and yellow people in thefuture We also need to collect samples from different agesranging from the infant the juvenile to the elderly Cur-rently our dataset includes only adults With a biggerdataset we can classify the samples into age gender raceand more We hope to set up a criterion of judgment formedical diagnosing Researchers and doctors can comparethe brain data of a patient with our data and confirm theabnormal proportion of tissues in the brain and furtherdiagnose what disease the patient has

To conclude we presented a method to successfullysegment the brain tissues from MRI using the convolutionalneural network e percentage results are very close to theaverage human brain data generated by the VCH modelis is a breakthrough since artificial intelligence and ma-chine learning have become more and more widely used inresearch By introducing deep learning into the therapeuticfield the speed and accuracy can be improved is is be-cause machines can automatically analyze the data whichcan be much faster and accurate than the manual andsemiautomatic analysis For future work we can visualizethe contours of the borders of different tissues in 3D so thatit can be integrated with optical simulation software such asMCVM for low-level light therapy Our work has a greatpotential in the medical field and we hope that our tech-nique can be a criterion of judgment for diagnosing

Data Availability

e MRI data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

is study was supported by the National Natural ScienceFund Project (no 61675039) the National Key RampD Pro-gram of China (nos 2017YFB1302305 and 2017YFB1300301)the Tianjin Key Project Grant for Natural Science (no18JCZDJC32700) and the CAMS Innovation Fund forMedical Sciences (no 2016-I2M-3-023)

References

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[2] P irumurugan and P Shanthakumar ldquoBrain tumor de-tection and diagnosis using ANFIS classifierrdquo International

Journal of Imaging Systems and Technology vol 26 no 2pp 157ndash162 2016

[3] E R Kandel J H Schwartz and T M Jessell Principles ofNeural Science United State of America McGraw-Hill NewYork PA USA 4th edition 2000

[4] N A Losseff L Wang H M Lai et al ldquoProgressive cerebralatrophy in multiple sclerosis A serial MRI studyrdquo Brainvol 119 no 6 pp 2009ndash2019 1996

[5] N C Fox R S Black S Gilman et al ldquoEffects of A im-munization (AN1792) onMRImeasures of cerebral volume inAlzheimer diseaserdquo Neurology vol 64 no 9 pp 1563ndash15722005

[6] P Demaeral C Faubert G Wilms P Casaer U Piepgrasand A L Baert ldquoMR findings in leukodystrophyrdquo Neuro-radiology vol 33 pp 368ndash371 1991

[7] T Kurokawa Y Chen M Nagata K Hasuo T Kobayashiand T Kitaguchi ldquoLate infantile krabbe leukodystrophy MRIand evoked potentials in a Japanese girlrdquo Neuropediatricsvol 18 no 3 pp 182-183 2008

[8] Z Kong T Li J Luo and S Xu ldquoAutomatic tissue imagesegmentation based on image processing and deep learningrdquoNeural Imaging and Sensing vol 63 2018

[9] M I Razzak S Naz and A Zaib Deep Learning for MedicalImage Processing Overview Challenges and Future 2017arXiv preprint arXiv170406825

[10] J Cheng and Y Zhao ldquoBrain NMR image segmentation al-gorithm based on deep learningrdquo Light Industry Science andTechnology vol 8 pp 93ndash97 2017

[11] C-C Lai ldquoA novel image segmentation approach based onparticle swarm optimizationrdquo IEICE Transactions on Fun-damentals of Electronics Communications and ComputerSciences vol E89-A pp 324ndash327 2006

[12] C Chu and J K Aggarwal ldquoe integration of image seg-mentation maps using region and edge informationrdquo IEEETransactions on Pattern Analysis and Machine Intelligencevol 15 pp 1241ndash1252 1993

[13] X Zhou D Chen and S Yuan ldquoMedical brain MRIimagessegmentation by improved fuzzy c-means clustering analysisrdquoJournal of Jilin University Engineering and Technology Edi-tion 2009

[14] B Miao and Y Hou ldquoMedical Image segmentation based onimproved fuzzy c-means clustering algorithmrdquo Laser Journalvol 36 no 1 2015

[15] V Srhoj-Egekher M J Benders M A Viergever and I IsgumldquoAutomatic neonatal brain tissue segmentation with MRIrdquo inProceedings of SPIE Medical Imaging International Societyfor Optics and Photonics Bellingham WA USA 2013

[16] P Moeskops M A Viergever A M Mendrik L S de VriesM J N L Benders and I Isgum ldquoAutomatic segmentation ofMR brain images with a convolutional neural networkrdquo IEEETransactions on Medical Imaging vol 35 no 5 pp 1252ndash1261 2016

[17] Y Lecun L Bottou Y Bengio and P Haffner ldquoGradient-basedlearning applied to document recognitionrdquo in Proceedings ofthe IEEE vol 86 11 pp 2278ndash2324 1998

[18] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquo Neural Computation vol 18pp 1527ndash1554 2006

[19] C Farabet C Couprie L Najman and Y LeCun ldquoLearninghierarchical features for scene labelingrdquo IEEE Transactions onPattern Analysis and Machine Intelligence vol 35 no 8pp 1915ndash1929 2013

[20] A Krizhevsky I Sutskever and G Hinton ImageNet Classi-fication with Deep Convolutional Neural Networks NIPS 2012


[21] A Mohamed T N Sainath G E Dahl et al ldquoDeep beliefnetworks using discriminative features for phone recogni-tionrdquo in Proceedings of 2011 IEEE International Conference onIEEE Acoustics Speech and Signal Processing (ICASSP)pp 5060ndash5063 Prague Czech Republic 2011

[22] G E Dahl Y Dong D Li et al ldquoLarge vocabulary continuousspeech recognition with context-dependent DBN-HMMSrdquo inProceedings of IEEE Internationl Conference on AcousticsSpeech and Signal Processing pp 4688ndash4691 Prague CzechRepublic 2011

[23] Z Akkus A Galimzianova A Hoogi D L Rubin andB J Erickson ldquoDeep learning for brain MRI segmentationstate of the art and future directionsrdquo Journal of DigitalImaging vol 1ndash11 2017

[24] W Zhang R Li H Deng et al ldquoDeep convolutional neuralnetworks for multi-modality isointense infant brain imagesegmentationrdquo Neuroimage vol 108 pp 214ndash224 2015

[25] L Yang Y Zhang J Chen S Zhang and Z Danny ldquoChensuggestive annotation a deep active learning framework forbiomedical image segmentationrdquo 2017 MICCAI arXiv170604737

[26] L Man H Jing and Y Feng ldquoMultimodal 3D convolutionalneural networks features for brain tumor segmentationrdquoScience Technology and Engineering vol 31 2014

[27] C Huang ldquoResearch of image denoising method aboutwavelet transform with neighborhood averagerdquo AdvancedMaterials Research vol 989-994 pp 4054ndash4057 2014

[28] S Wu Y Xue G Cheng et al ldquoResearch on K-meansclustering segmentation method for MRI brain image basedon selecting multi-peaks in grey histogramrdquo Journal of Bio-medical Engineering vol 30 no 6 pp 1164ndash1170 2013

[29] M Havaei A Davy D Warde-Farley et al ldquoBrain tumorsegmentation with deep neural networksrdquo Medical ImageAnalysis vol 35 pp 18ndash31 2017

[30] T Liu S Fang Y Zhao P Wang and J Zhang Imple-mentation of Training Convolutional Neural Networks 2015arXiv150601195

[31] W Gropp E Lusk N Doss and A Skjellum ldquoA high-performance portable implementation of the MPI messagepassing interface standardrdquo Parallel Computing vol 22 no 6pp 789ndash828 1996

[32] S Sahni and G Vairaktarakis ldquoe master-slave paradigm inparallel computer and industrial settingsrdquo Journal of GlobalOptimization vol 9 no 3-4 pp 357ndash377 1996

[33] I Sokolinskaya and L B Sokolinsky ldquoScalability evaluation ofNSLP algorithm for solving non-stationary linear pro-gramming problems on cluster computing systemsrdquo inSupercomputing RuSCDays Communications in Computerand Information Science V Voevodin and S Sobolev Edsvol 793 Springer Cham Switzerland 2017

[34] T Li Y Zhao Y Sun and M Duan ldquoEffects of wavelengthbeam type and size on cerebral low-level laser therapy by aMonte Carlo study on visible Chinese humanrdquo Journal ofInnovative Optical Health Sciences vol 8 no 1 article 1540022014

[35] T Li Y Li Y Sun M Duan and L Peng ldquoEffect of headmodel on Monte Carlo modeling of spatial sensitivity dis-tribution for functional near-infrared spectroscopyrdquo Journalof Innovative Optical Health Sciences vol 8 no 5 article1550024 2015

[36] T Li H Gong and Q Luo ldquoVisualization of light propa-gation in visible Chinese human head for functional near-infrared spectroscopyrdquo Journal of Biomedical Optics vol 16article 045001 2011

[37] L Wu Y Lin and T Li ldquoEffect of human brain edema onlight propagation a Monte Carlo modeling based on thevisible Chinese human datasetrdquo IEEE Photonics Journalvol 9 no 5 pp 1ndash10 2017

[38] T Li C Xue P Wang and L Wu ldquoPhoton penetration depthin human brain for light stimulation and treatment a RealisticMonte Carlo Simulation Studyrdquo Journal of Innovative OpticalHealth Sciences vol 10 no 5 article 1743002 2017

[39] T Li H Gong and Q Luo ldquoVisualization of light propa-gation in visible Chinese human head for functional near-infrared spectroscopyrdquo Journal of Biomedical Optics vol 16no 4 article 045001 2011

[40] C A Cocosco V Kollokian R K-S Kwan and A C EvansldquoBrainWeb online interface to a 3D MRI simulated brainDatabaserdquo in Proceedings of 3rd International Conference onFunctional Mapping of the Human Brain CopenhagenDenmark May 1997

[41] Y Ge R I Grossman J S Babb M L Rabin L J Mannonand D L Kolson ldquoAge-related total gGrey matter and whitematter changes in normal adult brain Part I volumetric MRimaging analysisrdquo American Journal of Neuroradiologyvol 23 no 8 pp 1327ndash1333 September 2002

[42] T Q Bartlett M W Vannier D W McKeel Jr M GadoC F Hildebolt and R Walkup ldquoInteractive segmentation ofcerebral grey matter white matter and CSF photographicand MR imagesrdquo Computerized Medical Imaging andGraphics vol 18 no 6 pp 449ndash460 1994

[43] FreeSurfer Wiki httpsurfernmrmghharvardedufswiki[44] M Hasanzadeh and S Kasaei ldquoMultispectral brain MRI

segmentation using genetic fuzzy systemsrdquo in Proceedings of9th International Symposium on Signal Processing and ItsApplications pp 1ndash4 Sharjah UAE February 2007


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For example the skull is a round structure that covers theother tissues is can help us to determine the location oftissues and get more accurate segmentation images As aresult threshold determination is often considered as anearly stage sequential image process Later methods relatedto fuzzy c-means (FCM) [13 14] and machine learning areintroduced e atlas-based method is also widely used forbrain image segmentation [15] It has a relatively completesystem framework However explicit information such asintensity and spatial features is required in order to getaccurate results [16] Spatial and intensity features could beavoided by using convolutional neural networks (CNNs)[16] Convolution neural network proposed by LeCun et al[17] is a deep supervised learning method [18] It has beenapplied in many fields and has made great success in imagerecognition [19 20] speech recognition [21 22] naturallanguage processing and so on CNNs obtain the convo-lution weight by means of cyclic convolution and sampleswith a supervised training mode e final realization isdirectly extracted from the original input which is con-ducive to the classification featurese features in the imagerecognition are texture shape and structure

MICCAI is a conference held every year focusing onmedical image computing and computer-assisted in-tervention [23] Recently many methods related to deeplearning were presented in the conference Zhang et alpresented a 2D patch-wise convolutional neural networks(CNNs) approach to segment tissues from multimodal MRimages of infants [24] An NlowastN size picture block wasextracted from a given image and the model is trained withthese blocks en the label was given to the correctidentification class In order to improve the performance ofblock training framework multiscale CNNs used a variety ofways with different patch sizes e outputs of these ap-proaches were combined with the neural network and themodel was trained to give the correct label is method inthis paper did not include a pooling layer or consider the

relation between the patches Yang et al [25] used a deepactive learning framework to reduce the annotation effort Itwas combined with fully convolutional network and activelearning Man et al [26] proposed combining MRI multi-modal information to extend CNNs to 3D which wascomposed of multiple modes that formed 3D raw data

In our paper we use image enhancement operators andmorphometry methods to extract the accurate contours ofdifferent tissues on 5MRI head image datasets After that weutilize convolutional neural network to realize automaticsegmentation of images with deep learning Such approachesgreatly reduced the processing time compared to the othermethods We also introduce parallel computing to furtherspeed up the processing speed Our work has a great po-tential in the medical field for diagnosing brain disease

e rest of the paper is organized as follows In Section 2we describe our dataset model and training method Ourexperiments and comparison with other methods are dis-cussed in Section 3 Section 4 concludes the paper

2 Materials and Methods

21 Dataset Our dataset includes 5 patientrsquos brain MRI T1-W images For every patient we have 160 images with a totalof 800 images e size of the images is 256 times 256 pixelsEvery pixel value in the matrix is an integer between 0 and255 Figure 2 shows some typical MRI of a human braineMRI data used to support the findings of this study areavailable from the corresponding author upon request

22 Preprocessing Due to the complicity of the brainstructure there exist many overlapping regions in each MRimage Image preprocessing can improve both the efficiencyof the algorithm and the reliability of the segmentationresults Image noise reduction [27] and enhancement canmake the image more conforming for viewing By removing

GM CSF

WM

(a)

Skull

(b)

Figure 1 MRI images (a) location of the 3 tissues WM is the area where the color is light GM is the gray boundary around theWM CSF isthe black parts inside the skull We focus on segmenting the tissues in the red circle (b) Cross section of a side of the brain the tissues arehard to distinguish by eyes










xlj f 1113944

iisinMj

xlminus1i lowast k

lij + b

lj

⎛⎜⎝ ⎞⎟⎠ (1)









45 times104

4

35

3

25

2

15

1

05

040 50 60 70 80

Grey level

Pixe

ls

90 100 110 120 130


Inputs32 times 32

C1 featuremaps

628 times 28

S2 featuremaps

614 times 14

C3 featuremaps

1610 times 10

S4 featuremaps

165 times 5C5 layer

120F6 layer

84Outputs

10

Fullyconnected

Fullyconnected








3 Results







Layer 3 lowast 3

Output









Master node

Slave node










With

out p

repr

oces

sing

(a)


With

pre

proc

essin

g

(b)


















5000

4000

3000

2000

1000

000CF GM WM

Tissue

Perc

enta

ge

Skull

VCHMRI

(a)

25

2

15

1

05

0VCH

Ratio

of G

MW

M

Our result

222 212

(b)









2635

2041

16581496 1428


Runt

ime (

s)

300

250

200

150

100






Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia










xlj f 1113944

iisinMj

xlminus1i lowast k

lij + b

lj

⎛⎜⎝ ⎞⎟⎠ (1)









45 times104

4

35

3

25

2

15

1

05

040 50 60 70 80

Grey level

Pixe

ls

90 100 110 120 130


Inputs32 times 32

C1 featuremaps

628 times 28

S2 featuremaps

614 times 14

C3 featuremaps

1610 times 10

S4 featuremaps

165 times 5C5 layer

120F6 layer

84Outputs

10

Fullyconnected

Fullyconnected








3 Results







Layer 3 lowast 3

Output









Master node

Slave node










With

out p

repr

oces

sing

(a)


With

pre

proc

essin

g

(b)


















5000

4000

3000

2000

1000

000CF GM WM

Tissue

Perc

enta

ge

Skull

VCHMRI

(a)

25

2

15

1

05

0VCH

Ratio

of G

MW

M

Our result

222 212

(b)









2635

2041

16581496 1428


Runt

ime (

s)

300

250

200

150

100






Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






45 times104

4

35

3

25

2

15

1

05

040 50 60 70 80

Grey level

Pixe

ls

90 100 110 120 130


Inputs32 times 32

C1 featuremaps

628 times 28

S2 featuremaps

614 times 14

C3 featuremaps

1610 times 10

S4 featuremaps

165 times 5C5 layer

120F6 layer

84Outputs

10

Fullyconnected

Fullyconnected








3 Results







Layer 3 lowast 3

Output









Master node

Slave node










With

out p

repr

oces

sing

(a)


With

pre

proc

essin

g

(b)


















5000

4000

3000

2000

1000

000CF GM WM

Tissue

Perc

enta

ge

Skull

VCHMRI

(a)

25

2

15

1

05

0VCH

Ratio

of G

MW

M

Our result

222 212

(b)









2635

2041

16581496 1428


Runt

ime (

s)

300

250

200

150

100






Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



3 Results







Layer 3 lowast 3

Output









Master node

Slave node










With

out p

repr

oces

sing

(a)


With

pre

proc

essin

g

(b)


















5000

4000

3000

2000

1000

000CF GM WM

Tissue

Perc

enta

ge

Skull

VCHMRI

(a)

25

2

15

1

05

0VCH

Ratio

of G

MW

M

Our result

222 212

(b)









2635

2041

16581496 1428


Runt

ime (

s)

300

250

200

150

100






Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






With

out p

repr

oces

sing

(a)


With

pre

proc

essin

g

(b)


















5000

4000

3000

2000

1000

000CF GM WM

Tissue

Perc

enta

ge

Skull

VCHMRI

(a)

25

2

15

1

05

0VCH

Ratio

of G

MW

M

Our result

222 212

(b)









2635

2041

16581496 1428


Runt

ime (

s)

300

250

200

150

100






Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia

















5000

4000

3000

2000

1000

000CF GM WM

Tissue

Perc

enta

ge

Skull

VCHMRI

(a)

25

2

15

1

05

0VCH

Ratio

of G

MW

M

Our result

222 212

(b)









2635

2041

16581496 1428


Runt

ime (

s)

300

250

200

150

100






Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia








2635

2041

16581496 1428


Runt

ime (

s)

300

250

200

150

100






Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





Data Availability




Acknowledgments


References


















































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


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