An improved support vector machine kernel for medical image retrieval system

Digital medical images take up most of the storage space in the medical database. Digital images are in the form of X-Rays, MRI, CT. These medical images are extensively used in diagnosis and planning treatment schedule. Retrieving required medical images from the database in an efficient manner for diagnosis, research and educational purposes is essential. Image retrieval systems are used to retrieve similar images from database by inputting a query image. Image retrieval systems extract features in the image to a feature vector and use similarity measures for retrieval of images from the database. So the efficiency of the image retrieval system depends upon the feature selection and its classification. In this paper, it is proposed to implement a novel feature selection mechanism using Discrete Sine Transforms (DST) with Information Gain for feature reduction. Classification results obtained from existing Support Vector Machine (SVM) is compared with the proposed Support Vector Machine model. Results obtained show that the proposed SVM classifier outperforms conventional SVM classifier and multi layer perceptron neural network.