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Computational advances applied to medical image processing: an update

Authors Joao A, Gambaruto A, Tiago J, Sequeira A

Received 25 August 2015

Accepted for publication 22 January 2016

Published 29 March 2016 Volume 2016:8 Pages 1—15


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Professor Alex MacKerell

Ana João,1 Alberto M Gambaruto,2 Jorge Tiago,1 Adélia Sequeira1

Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; 2Department of Mechanical Engineering, University of Bristol, Bristol, UK

Abstract: Medical imaging provides a high-fidelity, noninvasive, or minimally invasive means for effective diagnostic and routine checks, and has become an established tool in both clinical and research settings. The interpretation of medical images commonly requires analysis by an experienced individual with the necessary skills. This dependence on an individual's evaluation in part limits the broader scope and widespread use of medical images that would be possible if performed automatically. The analysis of medical images by an individual may also influence reliability, with different users attaining alternative conclusions from the data set. It is thus beneficial to support the experienced user with robust and fast processing of the medical images for further analysis that relies as little as possible on user interaction. In the existing body of literature, a variety of methods have been proposed for medical image filtering and enhancement, which have been largely used in the context of improving image quality for both human visual perception and feature detection and object segmentation via a numerical algorithm. In this study, an analysis of some popular methodologies for image processing is presented. From the comparison of results, a robust and automatic pipeline procedure for medical image processing is put forward, and results for different imaging-acquisition techniques are given.

Keywords: medical imaging, automatic image processing, image filtering, contrast enhancement, object segmentation, feature extraction

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