Abstract
In this paper we present a hybrid approach based on combining fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms to measure gray (GM) and white matter (WM) tissue. The proposed algorithm uses intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different clusters using fuzzy k-means clustering. The centers of these clusters are the input to the region growing (SRG) method for creating the closed regions. The outputs of SRG technique are fed to sensitivity and specificity algorithm to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results.
Highlights
Medical imaging includes conventional projection radiography, computed topography (CT), magnetic resonance imaging (MRI) and ultrasound
We present an approach based on combining fuzzy c-mean clustering, seed region growing, and sensitivity and specificity algorithm to determine gray matter (GM) and white matter (WM) tissues in brain MRIs
We have presented an approach for medical image segmentation, which integrates three existing methods: fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms
Summary
Medical imaging includes conventional projection radiography, computed topography (CT), magnetic resonance imaging (MRI) and ultrasound. MRI has several advantages over other imaging techniques enabling it to provide 3D data with high contrasts between soft tissues. The segmentation of region is an important first step for variety of image related applications and visualization tasks. Segmentation of medical images is important since it provides assistance for medical doctors to find out the diseases inside the body without the surgery procedure, to reduce the image reading time, to find the location of a lesion and to determine an estimate the probability of a disease. Segmentation of brain MRIs into different tissue classes, especially gray matter (GM), and white matter (WM), is an important task. Brain MRIs have low contrast between some different tissues. The problem of MRIs is the low contrast between tissues
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