Automatic Detection and Classification of Glioma Tumors using Statistical Features

1 Department of Electronics and Communication Engineering, College of Engineering Perumon Kollam, Kerala, INDIA 2 Department of Electronics, Cochin University of Science and Technology Kochi-22, Kerala, INDIA ______________________________________________________________________________________ Abstract: The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding pvalue. A decision system was developed for the grade detection of glioma using these selected features and its pvalue. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing.