Title: Integration of global and local features based on hybrid similarity matching scheme for medical image retrieval system

Authors: K.P. Ajitha Gladis

Addresses: Department of IT, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India

Abstract: Similarity measure is a challenging task in Content-Based Medical Image Retrieval (CBMIR) systems and the matching scheme is designed to improve the retrieval performance. However, there are several major shortcomings with conventional approaches for a matching scheme which can extensively affect their application of Medical Image Retrieval (MIR). To overcome the issues, in this paper a Multi-Level Matching (MLM) method for MIR using hybrid feature similarity is proposed. Here, images are represented by multi-level features including local level and global level. The Colour and Edge Directivity Descriptor (CEDD) is used as a colour and edge-based descriptor. Speeded-Up Robust Features (SURF) and Local Binary Pattern (LBP) are used as a local descriptor. The hybrid of both global and local features yields enhanced retrieval accuracy, which is analysed over collected image databases. From the experiment, the proposed method achieves better accuracy value about 92%, which is higher than other methods.

Keywords: CBIR; local features; global features; multi-level matching; hybrid; similarity; descriptor; CEDD; LBP; SURF.

DOI: 10.1504/IJBET.2019.102973

International Journal of Biomedical Engineering and Technology, 2019 Vol.31 No.3, pp.292 - 314

Received: 12 Nov 2016
Accepted: 23 Mar 2017
Published online: 14 Oct 2019 *

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