Biomedical Image Processing / Medical Image Processing
Abbas Biniaz; Ataollah Abbasi; Mousa Shamsi
Volume 7, Issue 2 , June 2013, , Pages 175-186
Abstract
Segmentation divides an image to some subdivisions where which of ones has similar intensity gray levels. Among clustering methods fuzzy c-means (FCM) clustering has been frequently used for segmentation of medical images. However, this algorithm doesn’t incorporate spatial neighborhood information ...
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Segmentation divides an image to some subdivisions where which of ones has similar intensity gray levels. Among clustering methods fuzzy c-means (FCM) clustering has been frequently used for segmentation of medical images. However, this algorithm doesn’t incorporate spatial neighborhood information in segmentation. This approach is very susceptible to nuisance factors. Therefore this paper proposes a Gaussian spatial FCM (gsFCM) to MR image segmentation. Proposed method has less sensitivity to noise specially in tissue boundaries, angles, and borders than spatial FCM (sFCM). Furthermore by the suggested algorithm a pixel which is a separate tissue from structurally point of view for example a tumor in primary stages of its appearance, has more chance to be a unique cluster. Applying quantitative assessments using Jaccard similarity index, Dice coefficient, and other validation functions on FCM,sFCM and gsFCM approaches show efficient performance of the proposed method. In this research the ISBR data bank is used for simmulations.Moreover in medical applications getting patient condition and information with fast methods is very important especially in emergency circumstances. Therefore all effective agents in patient health must be fast even medical algorithms such as clustering ones . Hence in this paper to decrease the time of convergence considerably and decline the number of iterations significantly, cluster centroids are initialized by an algorithm.
