A NOVEL FEATURE SELECTION MECHANISM FOR MEDICAL IMAGE RETRIEVAL SYSTEM

Computer assistance has reached virtually in every domain with in the field of medical imaging. Dedicated Computer aided diagnosis (CAD) tools with proven clinical impact exist for narrow range of applications. Medical imaging modalities such as X-Rays, CT, MRI, CT-PET, and PET provide visual information for accurate diagnosis and indexed medical treatment. Now a days Medical databases are used automatically to classify the visual features for retrieving image which provides a Indexed reference for easy therapy. Medical image retrieval provides an archive for identifying the similar features with the given query image. In this work it is proposed to implement a novel feature selection mechanism using discrete sine transform. This classification results use support vector machine (SVM) which classifies kernel function, Regression values, Synaptic weights, Activation functions using multilayer perceptron neural network. The results obtained are performed with noise and blur to obtain noise free image which is further computed with statistical values and histogram processing to determine the accuracy of similar feature extracted.