TELKOMNIKA Telecommunication, Computing, Electronics and Control
Contour evolution method for precise boundary delineation of medical images
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Contour evolution method for precise boundary delineation of medical images
Contour evolution method for precise boundary delineation of medical images
Subject
Boundary delineation, Contour evolution, Image segmentation, MRI images
Description
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.
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.
Creator
Friska Natalia, Hira Meidia, Nunik Afriliana, Julio Christian Young, Sud Sudirman
Source
DOI: 10.12928/TELKOMNIKA.v18i3.14746
Publisher
Universitas Ahmad Dahlan
Date
June 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Friska Natalia, Hira Meidia, Nunik Afriliana, Julio Christian Young, Sud Sudirman, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Contour evolution method for precise boundary delineation of medical images,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3827.
Contour evolution method for precise boundary delineation of medical images,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3827.