Selective Image Segmentation Models Using Three Distance Functions

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specif...

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Autores principales:	Abdul Kadir Jumaat, Siti Aminah Abdullah
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2021
Materias:	active contour convex distance selective segmentation convex functional selective variational image segmentation Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/2a359ab70a014c53906e835397458fd1
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spelling	oai:doaj.org-article:2a359ab70a014c53906e835397458fd12021-11-14T08:31:27ZSelective Image Segmentation Models Using Three Distance Functions10.32890/jict2022.21.1.51675-414X2180-3862https://doaj.org/article/2a359ab70a014c53906e835397458fd12021-11-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2022.21.1.5https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models. Abdul Kadir JumaatSiti Aminah AbdullahUUM Pressarticleactive contourconvex distance selective segmentationconvex functionalselective variational image segmentationInformation technologyT58.5-58.64ENJournal of ICT, Vol 21, Iss 1, Pp 95-116 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	active contour convex distance selective segmentation convex functional selective variational image segmentation Information technology T58.5-58.64
spellingShingle	active contour convex distance selective segmentation convex functional selective variational image segmentation Information technology T58.5-58.64 Abdul Kadir Jumaat Siti Aminah Abdullah Selective Image Segmentation Models Using Three Distance Functions
description	Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models.
format	article
author	Abdul Kadir Jumaat Siti Aminah Abdullah
author_facet	Abdul Kadir Jumaat Siti Aminah Abdullah
author_sort	Abdul Kadir Jumaat
title	Selective Image Segmentation Models Using Three Distance Functions
title_short	Selective Image Segmentation Models Using Three Distance Functions
title_full	Selective Image Segmentation Models Using Three Distance Functions
title_fullStr	Selective Image Segmentation Models Using Three Distance Functions
title_full_unstemmed	Selective Image Segmentation Models Using Three Distance Functions
title_sort	selective image segmentation models using three distance functions
publisher	UUM Press
publishDate	2021
url	https://doaj.org/article/2a359ab70a014c53906e835397458fd1
work_keys_str_mv	AT abdulkadirjumaat selectiveimagesegmentationmodelsusingthreedistancefunctions AT sitiaminahabdullah selectiveimagesegmentationmodelsusingthreedistancefunctions
_version_	1718429766332186624

Selective Image Segmentation Models Using Three Distance Functions

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