Tumor Segmentation and Gradation for MR Brain Images

Abstract

MRI is a non-invasive technology that is currently used for analyzing brain tumors, which can only conclusively be graded by invasive tissue extraction. Techniques like perfusion used for analysis of tumors are time taking, therefore, there is increased focus towards automated computational techniques to segment and grade tumors using standard MR sequences like T1 and T2 weighted images. These are intensity based images and are not enough to grade tumors, thus, are not used in clinical practice. This work automates tumor segmentation without the use of templates and training sets, by using vertical symmetry comparison. Once the tumor is segmented, fuzzy c means clustering is used on T1 and T2 maps, that are generated by using three intensity based sequences namely T1, T2 and proton density (PD) fat saturated images, for tumor grading. The clustered sections are compared with cerebral blood volume (CBV) images that are obtained by perfusion parameters to find correlation. 15 patients, 5 each from 4th, 3rd and 2nd grade tumor categories, were tested. Some correlation is seen between T1, T2 map values of tumor region and CBV values showing that this area can be explored further for tumor grading.