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Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation
Author(s) -
Prachya Bumrungkun,
Kosin Chamgthai,
Wisarn Patchoo
Publication year - 2021
Publication title -
journal of healthcare engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.509
H-Index - 29
eISSN - 2040-2309
pISSN - 2040-2295
DOI - 10.1155/2021/9528460
Subject(s) - active contour model , template , initialization , segmentation , boundary (topology) , level set (data structures) , similarity (geometry) , artificial intelligence , computer science , set (abstract data type) , sensitivity (control systems) , pattern recognition (psychology) , image segmentation , mathematics , image (mathematics) , mathematical analysis , engineering , electronic engineering , programming language
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.

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