• Bangal Vaishali Dinkar
• J.V.Shinde

Automatic segmentation of brain tumor using computer analysis aided diagnosis in clinical practice but it is still a challenging task, especially when there are lesions needing to be outlined. In the applications of image-based diagnosis and computer-aided lesion detection, image segmentation is an important procedure. Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. We have form a hybrid method combining the advantages of both unsupervised and supervised methods. In our system we basically used hybrid method this allows us to construct an initial lesion probability map. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection.

Lesion detection, Magnetic resonance imaging (MRI), Unsupervised and supervised methods.

"AUTOMATED LESION DETECTION ON MRI SCANS USING HYBRID METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.554-565, June 2019, Available :http://www.jetir.org/papers/JETIR1908548.pdf

"AUTOMATED LESION DETECTION ON MRI SCANS USING HYBRID METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp554-565, June 2019, Available at : http://www.jetir.org/papers/JETIR1908548.pdf