Content Based Image Retrieval in Biomedical Images Using SVM Classification with Relevance Feedback
Sukhada Aloni · 2013
Abstract- Content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image pre-filtering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. In this framework, the probabilistic outputs of a multiclass support vector machine (SVM) classifier as category prediction of query and database images are exploited at first to filter out irrelevant images, thereby reducing the search space for similarity matching. Images are classified at a global level according to their modalities based on different low-level, concept, and key point-based features. It is difficult to find a unique feature to compare images effectively for all types of queries. Hence, a query-specific adaptive linear combination of similarity matching approach is proposed by relying on the image classification and feedback information from users. These images constitute an important source of anatomical and functional information for the diagnosis of diseases, medical research, and education. Effectively and efficiently searching in these large image collections poses significant technical challenges as the characteristics of the biomedical images differ significantly from other general purpose images.