A Robust Feature Descriptor for Biomedical Image Retrieval

Biomedical image retrieval is a crucial side of computer-aided diagnosis. It helps the radiologist and medical specialist to spot and perceive the specific disease. This paper proposed an efficient approach for retrieving similar biomedical images based on the Zernikemoment features, curvelet features and histogram of oriented gradients (HOG) feature. The Zernike polynomials based moments set defines the Zernike moment which is a global descriptor and it is capable of extracting both texture and shape information with minimum redundant data. The curvelet transformation is used to computethe edge-based shape information in form of curvelet histogram for the curves with discontinuity and the HOG features calculate the happenings of gradient orientation in the local areas of an image. The experiments were conducted on four benchmark biomedical image databases: HRCT dataset, Emphysema CT database, OASIS MRI database and NEMA MRI database respectively. The performance of the proposed approach was compared with many existing methods and achieved a better retrieval rate on all the four databases. (C) 2020 AGBM. Published by Elsevier Masson SAS. All rights reserved.