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Boundary Detection in Medical Images Using Edge

Following Algorithm Based on Intensity Gradient

and Texture Gradient Features

ABSTRACT

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new

edge following technique for boundary detection in noisy images. Utilization of the proposed

technique is exhibited via its application to various types of medical images. Our proposed

technique can detect the boundaries of objects in noisy images using the information from the

intensity gradient via the vector image model and the texture gradient via the edge map. The

performance and robustness of the technique have been tested to segment objects in synthetic

noisy images and medical images including prostates in ultrasound images, left ventricles in

cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints

in computerized tomography images. We compare the proposed segmentation technique with the

active contour models (ACM), geodesic active contour models, active contours without edges,

gradient vector flow snake models, and ACMs based on vector field convolution, by using the

skilled doctors’ opinions as the ground truths. The results show that our technique performs very

well and yields better performance than the classical contour models. The proposed method is

robust and applicable on various kinds of noisy images without prior knowledge of noise

properties.

BLOCK

DIAGRAM:

 

Input image Edge

Map 

Edge Following

Technique 

Average Edge

Vector Field 

Initial Position 

Boundary

Detected

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EXISTING SYSTEM:

Active contour models (ACM), geodesic active contour models, active contours without edges,

gradient vector flow snake models.

DISADVANTAGES:

Detecting the correct boundaries of objects has difficulties in medical images in which ill-

defined edges are encountered.

PROPOSED SYSTEM:

Technique for boundary detection for ill-defined edges in noisy images using a novel edge

following.

ADVANTAGES:

Method is more efficient than the five contour models.

Domain

Digital Image Processing

Digital image processing is the use of computer algorithms to perform image processing

on digital images. As a subfield of digital signal processing, digital image processing has many

advantages over analog image processing; it allows a much wider range of algorithms to be

applied to the input data, and can avoid problems such as the build-up of noise and signal

distortion during processing.

SOFTWARE REQUIREMENTS

  MATLAB 7.9

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MATLAB

MATLAB is a high-performance language for technical computing. It integrates computation,

visualization, and programming in an easy-to-use environment where problems and solutions are

expressed in familiar mathematical notation.

Typical uses include:

  Math and computation

  Algorithm development

  Modeling, simulation, and prototyping

  Data analysis, exploration, and visualization

  Scientific and engineering graphics

  Application development, including Graphical User Interface building

MATLAB is an interactive system whose basic data element is an array that does not require

dimensioning. This allows you to solve many technical computing problems, especially those

with matrix and vector formulations, in a fraction of the time it would take to write a program in

a scalar non-interactive language such as C or FORTRAN.