A robust model using SIFT and gamma mixture model for texture images classification: Perspectives for medical applications

TitreA robust model using SIFT and gamma mixture model for texture images classification: Perspectives for medical applications
Publication TypeJournal Article
Year of Publication2020
AuthorsBenlakhdar, S, Rziza, M, Thami, ROH
JournalBiomedical and Pharmacology Journal
Volume13
Pagination1659-1669
Mots-clésArticle, curvelet transform, human, human experiment, noise, rotation, scale invariant feature transform
Abstract

The texture analysis of medical images is a powerful calculation tool for the discrimination between pathological and healthy tissue in different organs in medical images. Our paper proposes a novel approach named, GGD-GMM, based on statistical modeling in wavelet domain to describe texture images. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform (SIFT). Secondly, we implement the aforementioned algorithm and fit the result by using the finite Gamma Mixture Model (GMM). The results, obtained for two benchmark datasets, show that our proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models. Moreover, it displays others advantages relied to Noise-resistant and rotation invariant. Our algorithm could be useful for the analysis of several medical issues. © 2020 This is an Open Access article licensed under a Creative Commons license: Attribution 4.0 International (CC-BY). Published by Oriental Scientific Publishing Company

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100517546&doi=10.13005%2fBPJ%2f2041&partnerID=40&md5=8997e98fae845d62807a15ca1c749766
DOI10.13005/BPJ/2041
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