Improving relevance feedback for content based medical image retrieval

Abstract

Content-Based Image Retrieval (CBIR) refers to techniques that retrieve images based on their content, as opposed to based on metadata. A CBIR system performs indexing and retrieval tasks using features like color, texture and shape computed from images as opposed to using the whole images. In the medical field, content based image retrieval is used to aid radiologist to retrieve of images with similar contents. CBIR methods are usually developed for specific features of images, so that those methods are not readily applicable across different kinds of medical images. Content-Based Medical Image Retrieval (CBMIR) refers to techniques that retrieve images from medical image databases. A CBMIR system using the medical image features like Haralick features, Zernike moments, histogram intensity features and run-length features. In this study, CBMIR system With improved feature selection method is developed using a hybrid approach of branch and bound and artificial bee colony using the breast cancer, Brain tumor and thyroid images and classification is performed using Fuzzy based Relevance Vector Machine (FRVM) to form groups of relevant image features The Euclidean distance measurement is used to assess the similarity between query images and database images. A Relevance feedback method using diverse density algorithm is used to improve the performance of content-based medical image Retrieval. An improved feature selection method is used to reduces the existing system dimensionality curse problem and improve the performance of the system.