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Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours

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...

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Detalles Bibliográficos
Autores principales: Memon, Asif Aziz, Soomro, Shafiullah, Shahid, Muhammad Tanseef, Munir, Asad, Niaz, Asim, Choi, Kwang Nam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657700/
https://www.ncbi.nlm.nih.gov/pubmed/33204300
http://dx.doi.org/10.1155/2020/6317415
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author Memon, Asif Aziz
Soomro, Shafiullah
Shahid, Muhammad Tanseef
Munir, Asad
Niaz, Asim
Choi, Kwang Nam
author_facet Memon, Asif Aziz
Soomro, Shafiullah
Shahid, Muhammad Tanseef
Munir, Asad
Niaz, Asim
Choi, Kwang Nam
author_sort Memon, Asif Aziz
collection PubMed
description 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 PH(2) 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.
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spelling pubmed-76577002020-11-16 Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours Memon, Asif Aziz Soomro, Shafiullah Shahid, Muhammad Tanseef Munir, Asad Niaz, Asim Choi, Kwang Nam Comput Math Methods Med Research Article 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 PH(2) 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. Hindawi 2020-11-04 /pmc/articles/PMC7657700/ /pubmed/33204300 http://dx.doi.org/10.1155/2020/6317415 Text en Copyright © 2020 Asif Aziz Memon et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
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
title Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours
title_full Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours
title_fullStr Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours
title_full_unstemmed Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours
title_short Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours
title_sort segmentation of intensity-corrupted medical images using adaptive weight-based hybrid active contours
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657700/
https://www.ncbi.nlm.nih.gov/pubmed/33204300
http://dx.doi.org/10.1155/2020/6317415
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