Statistical Features Segmentation Technique For MR Images Of Brain’s Tumors


Medical image analysis has great significance in the field of treatment, especially in non-invasive and clinical studies. Medical imaging techniques and it analysis and diagnoses analysis tools enable the physicians and Radiologists to reach at a specific diagnosis. In this study, MR images have been used for discriminating the infected tissues from normal brain’s tissues. A semi-automatic segmentation technique based on 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. The first stage is based on utilizing the 1st order statistical futures histogram based features such as (the mean, standard deviation, and image entropy ) which is local in nature, 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 Gray Matter (GM) brain’s tissue was the main encountered problem in the first presented segmentation method. To overcome this problem, an adaptive multi-stage segmentation technique is presented, in which the mean value of each pre-segmented classes has been used to distinguish the tumor tissue from others. The segmentation process is followed by a 2nd order classification method to assign image pixels accurately to their regions, using the invariant moments parameters weighted together with the Co-Occurrence parameters. Different samples of MR images for normal and abnormal brains (i.e. T1 and T2-weighted) have been tested, for different patients.