基于模糊逻辑算法的乳腺超声肿瘤图像种子点自动定位研究

Tianyu Zhao, Hang Dai
{"title":"基于模糊逻辑算法的乳腺超声肿瘤图像种子点自动定位研究","authors":"Tianyu Zhao, Hang Dai","doi":"10.1145/3510858.3511380","DOIUrl":null,"url":null,"abstract":"Breast cancer is the second leading cause of cancer death in women, which seriously endangers women's health. Breast tumor segmentation is one of the most critical and difficult tasks. In order to realize the automatic and rapid positioning of seed points and meet the needs of real-time online image segmentation, this paper presents an automatic positioning method of seed points in ultrasound breast tumor images based on fuzzy logic algorithm. The fuzzy logic is innovatively introduced into neural network, which is combined with the method of recalibrating the importance of characteristic elements in attention mechanism. By defining a reasonable fuzzy membership function, different diffusion coefficients are adopted for different pixel gradients. Automatic reference points are obtained by using the automatic reference point selection method based on texture features of image gray level co-occurrence matrix. On this basis, the seed points are iteratively obtained by Mean−Shift algorithm, which is the tumor area. Simulation results show that the proposed method is superior to most existing fuzzy closed-value segmentation methods.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"85 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Automatic Location of Seed Points in Ultrasound Breast Tumor Images Based on Fuzzy Logic Algorithm\",\"authors\":\"Tianyu Zhao, Hang Dai\",\"doi\":\"10.1145/3510858.3511380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Breast cancer is the second leading cause of cancer death in women, which seriously endangers women's health. Breast tumor segmentation is one of the most critical and difficult tasks. In order to realize the automatic and rapid positioning of seed points and meet the needs of real-time online image segmentation, this paper presents an automatic positioning method of seed points in ultrasound breast tumor images based on fuzzy logic algorithm. The fuzzy logic is innovatively introduced into neural network, which is combined with the method of recalibrating the importance of characteristic elements in attention mechanism. By defining a reasonable fuzzy membership function, different diffusion coefficients are adopted for different pixel gradients. Automatic reference points are obtained by using the automatic reference point selection method based on texture features of image gray level co-occurrence matrix. On this basis, the seed points are iteratively obtained by Mean−Shift algorithm, which is the tumor area. Simulation results show that the proposed method is superior to most existing fuzzy closed-value segmentation methods.\",\"PeriodicalId\":6757,\"journal\":{\"name\":\"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)\",\"volume\":\"85 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3510858.3511380\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3510858.3511380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

乳腺癌是妇女癌症死亡的第二大原因,严重危害妇女健康。乳腺肿瘤的分割是最关键和最困难的任务之一。为了实现种子点的自动快速定位,满足实时在线图像分割的需要,本文提出了一种基于模糊逻辑算法的乳腺超声肿瘤图像中种子点的自动定位方法。创新性地将模糊逻辑引入到神经网络中,并与注意机制中特征元素重要性的再标定方法相结合。通过定义合理的模糊隶属函数,对不同的像素梯度采用不同的扩散系数。采用基于图像灰度共现矩阵纹理特征的自动参考点选择方法获得自动参考点。在此基础上,通过Mean−Shift算法迭代得到种子点,即肿瘤区域。仿真结果表明,该方法优于现有的模糊闭值分割方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Automatic Location of Seed Points in Ultrasound Breast Tumor Images Based on Fuzzy Logic Algorithm
Breast cancer is the second leading cause of cancer death in women, which seriously endangers women's health. Breast tumor segmentation is one of the most critical and difficult tasks. In order to realize the automatic and rapid positioning of seed points and meet the needs of real-time online image segmentation, this paper presents an automatic positioning method of seed points in ultrasound breast tumor images based on fuzzy logic algorithm. The fuzzy logic is innovatively introduced into neural network, which is combined with the method of recalibrating the importance of characteristic elements in attention mechanism. By defining a reasonable fuzzy membership function, different diffusion coefficients are adopted for different pixel gradients. Automatic reference points are obtained by using the automatic reference point selection method based on texture features of image gray level co-occurrence matrix. On this basis, the seed points are iteratively obtained by Mean−Shift algorithm, which is the tumor area. Simulation results show that the proposed method is superior to most existing fuzzy closed-value segmentation methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信