A Short Note On Mr Image Classification Using Adaboost

2265 Words May 28th, 2016 10 Pages
MR Image Classification Using Adaboost
For Brain Tumor Types

Priyanka B. Zaware Electronics and Telecommunication Engineering
P.E.S Modern COE, Pune University
Pune, India priyanka30991@gmail.com Prof. Rupali S. Kamathe Electronics and Telecommunication Engineering
P.E.S Modern COE, Pune University Pune, India rupalikamathe@gmail.com Abstract — Magnetic resonance imaging (MRI) is an crucial and most important technique used in the detection and classification of brain tumor. Brain MR imaging plays very a crucial role for radiologist to diagnose and treat brain tumour. Study of medical image by the radiologist is very time consuming and also the accuracy depends upon their experience and their expertise in that field. Thus computer aided systems become very necessary as they overcome the limitation.
This project presents an automated system of classification of tumor from brain MRI. The algorithm uses T2-weighted MRI images. The useful and important features of image are extracted from medical image for classification purpose. Here texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) method. The classification of MR images is done using Adaboost classifier. Then finally the performance of classifier is evaluated by sensitivity, specificity, error rate and accuracy.

Keywords— Brain MRI, computer aided systems, feature extraction, GLCM, Adaboost classifier.

INTRODUCTION
When most of the normal cells grow in our body gets old…
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