Statistical Features Segmentation Technique For MR Images Of Brain’sTumors - Ali, Saleh; Mahmood, Faleh (Iraqi Journal of Science, 2024)
Medical image analysis has great significance in the field of treatment, especiallyin non-invasive and clinical studies. Medical imaging techniques and it analysis anddiagnoses analysis tools enable the physicians and Radiologists to reach at a specificdiagnosis. In this study, MR images have been used for discriminating the infectedtissues from normal brain’s tissues. A semi-automatic segmentation technique basedon statistical futures has been introduced to segment the brain’s MR image tissues.The proposed system used two stages for extracting the image texture features. Thefirst stage is based on utilizing the 1st order statistical futures histogram basedfeatures such as (the mean, standard deviation, and image entropy ) which is local innature, while the second stage is based on utilizing the 2nd order statistical futures(i.e Co-Occurrence matrices features).Similar coloring and semi-equal statistical features of the tumor area and the GrayMatter (GM) brain’s tissue was the main encountered problem in the first presentedsegmentation method. To overcome this problem, an adaptive multi-stagesegmentation technique is presented, in which the mean value of each pre-segmentedclasses has been used to distinguish the tumor tissue from others. The segmentationprocess is followed by a 2nd order classification method to assign image pixelsaccurately to their regions, using the invariant moments parameters weightedtogether with the Co-Occurrence parameters. Different samples of MR images fornormal and abnormal brains (i.e. T1 and T2-weighted) have been tested, for differentpatients.