抽象的な

AUTOMATIC LESION SEGMENTATION OF MULTIPLE SCLEROSIS IN MRI IMAGES USING SUPERVISED CLASSIFIER

S.Sivagowri, Dr.M.C.Jobin Christ

Magnetic Resonance Imaging (MRI) can be used to detect lesions in the brains of Multiple Sclerosis (MS) patients and is imperative for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly. We proposed model for automatic segmentation of multiple sclerosis lesions from brain MRI data. These techniques use a supervised classifier that is trained Support Vector Machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The main contribution of this set of frameworks is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. As a result, the sensitivity for detection of MS lesions was 81.5% with 2.9 false positives per slice based on a leave-one-candidate-out test, and the similarity index between MS regions determined by the proposed method and neuroradiologists. These results indicate the proposed method would be useful for assisting neuroradiologists in assessing the MS in clinical practice.