Natalia, F, Meidia, H, Afriliana, N, Young, JC and Sudirman, S (2020) Contour evolution method for precise boundary delineation of medical images. TELKOMNIKA : Telecommunication, Computing, Electronics and Control, 18 (3). pp. 1621-1632. ISSN 1693-6930

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Abstract

Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.

Item Type:	Article
Uncontrolled Keywords:	Boundary Delineation; Contour Evolution; Image Segmentation; MRI Images
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General)
Divisions:	Computer Science & Mathematics
Publisher:	Institute of Advanced Engineering and Science
Date Deposited:	06 May 2020 11:27
Last Modified:	04 Sep 2021 07:21
DOI or ID number:	10.12928/telkomnika.v18i3.14746
URI:	https://researchonline.ljmu.ac.uk/id/eprint/12886

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