Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified differential evolution

分割 阈值 计算机科学 水准点(测量) 人工智能 差异进化 计算机视觉 模式识别(心理学) 图像分割 图像(数学) 大地测量学 地理
作者
Lili Ren,Dong Zhao,Xuehua Zhao,Weibin Chen,Lingzhi Li,TaiSong Wu,Guoxi Liang,Zhennao Cai,Suling Xu
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:148: 105910-105910 被引量:40
标识
DOI:10.1016/j.compbiomed.2022.105910
摘要

The effective analytical processing of pathological images is crucial in promoting the development of medical diagnostics. Based on this matter, in this research, a multi-level thresholding segmentation (MLTS) method based on modified different evolution (MDE) is proposed. The MDE is the primary benefit offered by the suggested MLTS technique, which is a novel proposed evolutionary algorithm in this article with significant convergence accuracy and the capability to leap out of the local optimum (LO). This optimizer came into being mostly as a result of the incorporation of the movement mechanisms of white holes, black holes, and wormholes into various evolutions. Thus, the developed MLTS approach may provide high-quality segmentation results and is less susceptible to segmentation process stagnation. To validate the efficacy of the presented approaches, first, the performance of MDE is validated using 30 benchmark functions, and then the proposed segmentation method is empirically compared with other comparable methods using standard pictures. On the basis of breast cancer and skin cancer pathology images, the developed segmentation method is compared to other competing methods and experimentally validated in further detail. By analyzing experimental data, the key compensations of MDE are proven, and it is experimentally shown that the unique MDE-based MLTS approach can achieve good performance in terms of many performance assessment indices. Consequently, the proposed method may offer an efficient segmentation procedure for pathological medical images.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
启程牛牛完成签到,获得积分10
1秒前
大爷发布了新的文献求助10
2秒前
Getlogger完成签到,获得积分10
2秒前
2秒前
yundong完成签到,获得积分10
6秒前
科研通AI5应助Karen_Liu采纳,获得10
6秒前
小猪要快乐完成签到,获得积分10
7秒前
晓晓马儿完成签到 ,获得积分10
10秒前
天天摸鱼完成签到,获得积分10
10秒前
10秒前
11秒前
量子星尘发布了新的文献求助10
12秒前
现代雁桃完成签到,获得积分10
12秒前
科研通AI2S应助lvsehx采纳,获得10
12秒前
吕大本事完成签到,获得积分10
13秒前
温柔的语柔完成签到,获得积分10
14秒前
阳光的荠完成签到 ,获得积分10
15秒前
Hancock完成签到 ,获得积分0
15秒前
15秒前
16秒前
17秒前
18秒前
19秒前
W29完成签到,获得积分0
20秒前
大爷发布了新的文献求助10
22秒前
王梅完成签到,获得积分10
23秒前
23秒前
asjm完成签到 ,获得积分10
23秒前
岁月星辰发布了新的文献求助10
24秒前
24秒前
25秒前
77完成签到,获得积分10
25秒前
Selonfer完成签到,获得积分10
26秒前
萂昕完成签到 ,获得积分10
27秒前
NexusExplorer应助wqy采纳,获得10
27秒前
spy完成签到,获得积分10
27秒前
nn完成签到 ,获得积分10
29秒前
29秒前
无情山水发布了新的文献求助10
29秒前
小马甲应助执着的导师采纳,获得10
31秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Zeolites: From Fundamentals to Emerging Applications 1500
Encyclopedia of Materials: Plastics and Polymers 1000
Architectural Corrosion and Critical Infrastructure 1000
Early Devonian echinoderms from Victoria (Rhombifera, Blastoidea and Ophiocistioidea) 1000
Hidden Generalizations Phonological Opacity in Optimality Theory 1000
Handbook of Social and Emotional Learning, Second Edition 900
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4919033
求助须知:如何正确求助?哪些是违规求助? 4191171
关于积分的说明 13016365
捐赠科研通 3961419
什么是DOI,文献DOI怎么找? 2171659
邀请新用户注册赠送积分活动 1189623
关于科研通互助平台的介绍 1098231