期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2011
卷号：3
期号：02
页码：980-990
出版社：Engg Journals Publications
摘要：Segmentation of medical imagery remains as a challenging task due to complexity of medical images. This study proposes a method of k-Nearest Neighbor (k-NN) in abnormalities segmentation of Magnetic Resonance Imaging (MRI) brain images. A preliminary data analysis is performed to analyze the characteristics for each brain component of �membrane�, �ventricles�, �light abnormality� and �dark abnormality� by extracting the minimum, maximum and mean grey level pixel values. The segmentation is done by executing five steps of k-NN which are determination of k value, calculation of Euclidian distances objective function, sortation of minimum distance, assignment of majority class, and determination of class based on majority ranking. The k-NN segmentation performances is tested to hundred and fifty controlled testing data which designed by cutting various shapes and size of various abnormalities and pasting it onto normal brain tissues. The tissues are divided into three categories of �low�, �medium� and �high� based on the grey level pixel value intensities. The overall experimental result returns good and promising segmentation outcomes for both light and dark abnormalities.
关键词：k-Nearest Neighbor (k-NN); brain abnormalities segmentation; Magnetic Resonance Imaging (MRI)
