Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI

Abstract

This paper explores the possibility of applying techniques for segmenting the regions of med ical image. For this we need to investigate the use of different techniques which helps for detection and classification of image regions. We also discuss some segmentation methods classified by researchers. Region classification is an essential process in the visualizat ion of brain t issues of MRI. Brain image is basically classified into three regions; WM, GM and CSF. The forth region can be called as the tumor region, if the image is not normal. In the paper; Segmentation and characterization of Brain M R image regions using SOM and neuro fuzzy techniques, we integrate Self Organizing Map(SOM) and Neu ro Fu zzy scheme to automatically extract WM, GM , CSF and tumor reg ion of brain MRI image tested on three normal and three abnormal brain M RI images. Now in this paper this scheme is further tested on axial v iew images to classify the regions of brain M RI and compare the results fro m the Keith"s database. Using some statistical tests like accuracy, precision, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, false negative rate, likelihood ratio positive, likelihood ratio negative and prevalence of disease we calculate the effectiveness of the scheme.

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