Multi-dimensional multi-directional mask maximum edge pattern for bio-medical image retrieval

Abstract

Authors have proposed novel multi-dimensional multi-directional mask maximum edge patterns for the bio-medical image retrieval. Standard local binary patterns encode relationship of neighbor pixels with center pixel. Local mesh patterns encode the relationship between adjacent pixels surrounding the center pixel. Proposed approach encodes relationship of neighbour pixels in adjacent planes of a multi-dimensional image, in three stages. In the first stage, five sub images are formed by traversing in five different directions on three planes of a multi-dimensional image. In the second stage, directional masks are applied on each sub image to find directional edges. In stage three, maximum edge patterns are found based on the directions of the directional edges. To examine performance analysis of the proposed algorithm, we tested proposed algorithm on three benchmark databases, which gives retrieval accuracy $$56.93\%$$ for top 5 images, 93.36 and $$62.49\%$$ for top 10 images on MESSIDOR (Retinal images), VIA/I-ELCAP (CT images) and OASIS-MRI databases respectively in terms of average retrieval precision. The comparison reflects, there is considerable improvement in the performance.