Selective Image Segmentation Models Using Three Distance Functions

Abdullah, Siti Aminah and Jumaat, Abdul Kadir (2022) Selective Image Segmentation Models Using Three Distance Functions. Journal of Information and Communication Technology, 21 (01). pp. 95-116. ISSN 2180-3862

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Abstract

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 concerned with 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 proposes 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 are CDSSNEW1, CDSSNEW2, and CDSSNEW3, which apply 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 (JSC) and Dice Similarity Coefficients (DSC). 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 the specific object successfully for all grayscale images with the fastest execution time compared to other models.

Item Type:	Article
Uncontrolled Keywords:	Active contour, convex distance selective segmentation, convex functional, selective variational image segmentation
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	College of Arts and Sciences
Depositing User:	Mrs Nurin Jazlina Hamid
Date Deposited:	27 Jul 2022 01:56
Last Modified:	01 Feb 2023 00:38
URI:	https://repo.uum.edu.my/id/eprint/28754

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