White Patch Detection in Brain MRI Image Using Evolutionary Clustering Algorithm

White Patch Detection in Brain MRI Image Using Evolutionary Clustering Algorithm

Pradeep Kumar Mallick (St. Peter's University, India), Mihir Narayan Mohanty (SOA University, India) and S. Saravana Kumar (Sree Vidyanikethan Engineering College, India)
DOI: 10.4018/978-1-4666-8737-0.ch018
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Abstract

Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this paper, an attempt is taken to analyse a CT brain image. It has been segmented for a particular patch in the brain CT image that may be one of the tumours in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it.In this paper both the concept of clustering and thresholding technique with edge based segmentation methods like sobel, prewitt edge detectors is applied. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.
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1. Introduction

Over last two decades bio-image analysis and processing occupied an important position. Image segmentation is the process of distinguishing the objects and background in an image. It is an essential preprocessing task for many applications that depend on computer vision such as medical imaging, locating objects in satellite images, machine vision, fingerprint and face recognition, agricultural imaging and other many applications. The accuracy of image segmentation stage would have a great impact on the effectiveness of subsequent stages of the image processing. Image segmentation problem has been studied by many researchers for several years; however, due to the characteristics of the images such as their different modal histograms, the problem of image segmentation is still an open research issue and so further investigation is needed.

Identifying specific organs or other features in medical images require a considerable amount of expertise concerning the shapes and locations of anatomical features. Such segmentation is typically performed manually by expert physicians as part of treatment planning and diagnosis. Due to the increasing amount of available data and the complexity of features of interest, it is becoming essential to develop automated segmentation methods to assist and speedup image-understanding tasks. Medical imaging is performed in various modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, etc. Several automated methods have been developed to process the acquired images and identify features of interest, including intensity-based methods, region-growing methods and deformable contour models. Intensity-based methods identify local features such as edges and texture in order to extract regions of interest. Region-growing methods start from a seed-point (usually placed manually) on the image and perform the segmentation task by clustering neighboring pixels using a similarity criterion. Deformable contour models are shape-based feature search procedures in which a closed contour deforms until a balance is reached between its internal energy (smoothness of the curve) and external energy (local region statistics such as first and second order moments of pixel intensity). The genetic algorithm framework brings considerable flexibility into the segmentation procedure by incorporating both shape and texture information. In the following sections we describe our algorithm in depth and relate our methodology to previous work in this area.

W. Pratt, Rafael C et.al, and A.K Jain provide the fundamental of image segmentation. A.Haldar et. al. described automatic image segmentation using fuzzy c-means clustering algorithm. Similarly A. Jyoti et.al. has been described CT brain image segmentation using clustering methods for effective and accurate feature extraction even in the presence of noise. Manoj K et. al., S.Lakshmi I et.al., S.Behera et. al., V.Rani et.al. and Muthukrishnan.R et. al. have beeb proposed various edge detection techniques for image segmentation. S. K. Kar et. al. discussed a statistical approach for recognition of color of the object. Here threshold is determined based on basic statistical method that leads to color recognition of an object. Applying the method of thresholding iteratively over the ROI selected, the recognition of the color of the desired object is performed.

Woo-seok Jang et.al proposed Optimized Fuzzy Clustering By Predator Prey Particle Swarm Optimization .Here fuzzy clustering is optimised using predator prey particle swarm optimizations (PPPSO). In order to avoid local optimal solutions and find global optimal solution efficiently. The performance of fuzzy c-means (FCM), particle swarm fuzzy clustering (PSFC) and predator prey particle swarm fuzzy clustering (PPPSFC) are compared. P. K. Sahoo et. al., Liu Jianzhuang Xidian Univ et.al, M. Cheriet et.al have been described various thresholding techniques of Image processing .

In this paper an attempt is made to segment an image using fuzzy c-means clustering algorithm with its modified objective function by considering the approach of genetic algorithm. This proposed method minimizes the objective function better than the conventional FCM and gives superior quality of segmented result.

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