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

Abstract

Since hospitals are generating and using image data extensively, medical image 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 into 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.