Tumor Classification and Segmentation of MR Brain Images

TL;DR

This paper presents a novel automated method for tumor classification and segmentation in brain MR images, achieving high accuracy without requiring training sets or templates, thus aiding faster and more reliable diagnosis.

Contribution

It introduces a unique combination of contralateral approach and patch thresholding for segmentation, and employs entire slices for classification, enhancing accuracy and simplicity.

Findings

Tumor classification accuracy of 95%

Segmentation achieved 100% specificity and 90% sensitivity

Method does not require training set or templates

Abstract

The diagnosis and segmentation of tumors using any medical diagnostic tool can be challenging due to the varying nature of this pathology. Magnetic Reso- nance Imaging (MRI) is an established diagnostic tool for various diseases and disorders and plays a major role in clinical neuro-diagnosis. Supplementing this technique with automated classification and segmentation tools is gaining importance, to reduce errors and time needed to make a conclusive diagnosis. In this paper a simple three-step algorithm is proposed; (1) identification of patients that present with tumors, (2) automatic selection of abnormal slices of the patients, and (3) segmentation and detection of the tumor. Features were extracted by using discrete wavelet transform on the normalized images and classified by support vector machine (for step (1)) and random forest (for step (2)). The 400 subjects were divided in a 3:1 ratio between training and test with no overlap. This study is novel in terms of use of data, as it employed the entire T2 weighted slices as a single image for classification and a unique combination of contralateral approach with patch thresholding for segmentation, which does not require a training set or a template as is used by most segmentation studies. Using the proposed method, the tumors were segmented accurately with a classification accuracy of 95% with 100% specificity and 90% sensitivity.