Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours
Joint Authors
Soomro, Shafiullah
Memon, Asif Aziz
Shahid, Muhammad Tanseef
Munir, Asad
Niaz, Asim
Choi, Kwang Nam
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-04
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model.
However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities.
To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images.
The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast.
The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation.
The weight function suppresses false contour evolution and also regularizes object boundaries.
Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images.
Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed.
Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.
American Psychological Association (APA)
Memon, Asif Aziz& Soomro, Shafiullah& Shahid, Muhammad Tanseef& Munir, Asad& Niaz, Asim& Choi, Kwang Nam. 2020. Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139514
Modern Language Association (MLA)
Memon, Asif Aziz…[et al.]. Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1139514
American Medical Association (AMA)
Memon, Asif Aziz& Soomro, Shafiullah& Shahid, Muhammad Tanseef& Munir, Asad& Niaz, Asim& Choi, Kwang Nam. Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139514
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139514