Evolutionary Multi-level Thresholding for Breast Thermogram Segmentation

被引：1
|
作者：
Tiwari, Arti [1 ]
Bhattacharjee, Kamanasish [1 ]
Pant, Millie [1 ]
Nowakova, Jana [2 ]
Snasel, Vaclav [2 ]
机构：
[1] Indian Inst Technol Roorkee, Dept Appl Sci & Engn, Roorkee 247667, Uttarakhand, India
[2] VSB Tech Univ Ostrava, Dept Comp Sci, 17 Listopadu 2172-15, Ostrava 70800, Czech Republic
来源：
ADVANCES IN INTELLIGENT NETWORKING AND COLLABORATIVE SYSTEMS (INCOS-2021) | 2022年 / 312卷
关键词：
ARTIFICIAL BEE COLONY; OPTIMIZATION; ALGORITHM;
D O I：
10.1007/978-3-030-84910-8_27
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the pre-processing of the digital thermograms, multi-level thresholding plays a crucial role in the segmentation of thermographic images for better clinical decision support. This paper attempts to optimize the multi-level thresholding method for thermographic image segmentation using Differential Evolution (DE) with the Otsu's between class variance. We have compared the results of the proposed method with the other popular metaheuristics- PSO, GWO and WOA. We have applied the Wilcoxon rank-sum test for the performance evaluation.
引用
收藏
页码：253 / 263
页数：11
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