Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation

Author:

Bumrungkun Prachya1,Chamnongthai Kosin1ORCID,Patchoo Wisarn2ORCID

Affiliation:

1. Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Road, Bangmod, Thung Khru, Bangkok 10140, Thailand

2. School of Engineering, Bangkok University, Bangkok 10110, Thailand

Abstract

For active-contour-based surgery systems, the success of skin cancer boundary segmentation depends on the initialization point of the snake model, which is a task originally performed by skillful experts, and on the parameters set for the algorithms of active contour. This paper proposes initial geometrical templates and parameter sets for the active contour on skin cancer boundary segmentation. To establish initial geometrical templates and parameter sets for the active contour, first, template candidates, which are geometrically designed by users in advance, are simply calculated based on similarity with a skin cancer boundary, and the candidate with the least difference is selected as an initial template. Initially, all candidate templates are performed before the test with some selected skin cancer samples by randomly varying needed parameters to determine parameter sets for each template. The parameter set is therefore implicitly selected as the suitable set with the selected initial template. Experiments with 227 skin cancer samples were performed based on our proposed initial templates and parameter sets, and the results show 99.46% accuracy, 97.43% sensitivity, and 99.87% specificity approximately in which accuracy, sensitivity, and specificity were improved by 0.26%, 0.36%, and 0.26%, respectively, compared with the conventional method.

Funder

Rajamangala University of Technology Isan (RMUTI), Surin Campus

Publisher

Hindawi Limited

Subject

Health Informatics,Biomedical Engineering,Surgery,Biotechnology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analysis, Prediction and Classification of Skin Cancer using Artificial Intelligence - A Brief Study and Review;Scalable Computing: Practice and Experience;2023-09-10

2. Skin Lesion Segmentation Using SCU-Net with FNLM Preprocessing;2022 IEEE International Conference on Data Science and Information System (ICDSIS);2022-07-29

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