Paper Title
Classification Of Human Brain Magnetic Resonance Images In Cbir Based On Saliency Map Of Images

Abstract
Content-based image retrieval (CBIR) makes use of low-level image features, such as color, texture and shape, to index images with minimal human interaction. As the growth and development of various multimedia technologies in the field of CBIR, many advanced information retrieval systems have become popular and has brought the new evolution in fast and effective retrieval. This paper presents a new method for classification of human brain magnetic resonance images (MRIs) based on images� saliency in an image retrieval system. The proposed method can reduce the gap between low-level image features and the high-level semantic concepts in the CBIR and improve the retrieval process. The saliency map of an image contains important image regions which are visually more conspicuous by virtue of their contrast with respect to surrounding regions. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image's saliency through ants' movements on the image and the textural features are calculated from the saliency map of the images. Then, the image classification is done with an adaptive neuro fuzzy inference system (ANFIS), which can categorize the magnetic resonance images as normal or tumoral. In online image retrieval, a query image is introduced to the system and the system will return the relevant images. The experimental result shows the accuracy of 98.67% for the image retrieval in our proposed system and improves the retrieval efficiency in compare with the classical CBIR systems. Index Terms- Content based image retrieval; Saliency map; ANFIS; Magnetic resonance image