Multi-Trend Structure Descriptor At Micro-Level For Histological Image Retrieval

Abstract

Abstract: Since hospitals are generating and using image data extensively, medical im age databases and its size are rising rapidly. This led to difficulties in browsing and managing the huge databases. Therefore, the necessity for the development of efficient content-based medical image retrieval (CBMIR) system arises and is more challenging problem for researchers. In this paper, to alleviate the unbalanced distribution of image representation using multi-trend structure descriptor (MTSD), MTSD is computed at micro level i.e., image is divided int o number of sub-images and for each sub-image MTSD is exploited. In similarity measurement, we compared the MTSDs of corresponding sub-images in query and target images than the liner ordered collection of smallest similarity values between the sub-images are considered for retrieval. Experiments revels that computation of proposed feature at micro level retains the localized representation and considering the liner ordered collection of smallest similarity values between the sub-images provides consistency under illumination changes and noise and thus proposed CBMIR achieves better results. Pattern analysis, Image retrieval and classification. His research interests include Machine learning algorithms and Medical imageanalysis. He has published more than 25 papers in various International journals and more than 15 papers in national and international conferences. He has been on the reviewing and editorial board of many reputed journals.