  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.
