An Efficient Content-Based Medical Image Retrieval System For Clinical Decision Support In Brain Tumor Diagnosis

Abstract

Accurate diagnosis is crucial for successful treatment of a brain tumor. Content Based Medical Image Retrieval (CBMIR) can assist radiologist in diagnosis by retrieving similar images from medical image database. Here a novelmethdology CBMIR for brain tumor is proposed. Magnetic Resonance imaging (MRI) is most commonly used for imaging the brain tumor. During the image acquisition there can be misalignment of MR images due to movement of patient and also low level semantics from MR image may not corresponds with high level semantics of brain tumor, for this two level CBMIR system used, which first classifies (using SVM and ANN) query image of brain tumor as cancerous and non-cancerous tumor using global feature (circularity, irregularity and texture feature) and then search for most similar images with identified class using local feature. This experiment has been performed on 294 brain MR images and result of classification is compare with precision rate, accuracy and recall rate.