基于图像分解的超声图像模糊聚类分割

Yan Xu
{"title":"基于图像分解的超声图像模糊聚类分割","authors":"Yan Xu","doi":"10.1109/ISIEA.2009.5356492","DOIUrl":null,"url":null,"abstract":"Ultrasound image segmentation is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, we propose a segmentation scheme using fuzzy c-means (FCM) clustering incorporating spatial information based on image decomposition. First, an ultrasound image is decomposed into a sum of two functions, u+v, where u denotes the image intensity while v refers to the texture. And then, a spatial FCM clustering method is applied on the image intensity component for segmentation. In the experiments with simulated and clinical ultrasound images, the proposed method can get more accurate results than other preprocessing or segmentation methods.","PeriodicalId":6447,"journal":{"name":"2009 IEEE Symposium on Industrial Electronics & Applications","volume":"1 1","pages":"6-10"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Image decomposition based ultrasound image segmentation by using fuzzy clustering\",\"authors\":\"Yan Xu\",\"doi\":\"10.1109/ISIEA.2009.5356492\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ultrasound image segmentation is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, we propose a segmentation scheme using fuzzy c-means (FCM) clustering incorporating spatial information based on image decomposition. First, an ultrasound image is decomposed into a sum of two functions, u+v, where u denotes the image intensity while v refers to the texture. And then, a spatial FCM clustering method is applied on the image intensity component for segmentation. In the experiments with simulated and clinical ultrasound images, the proposed method can get more accurate results than other preprocessing or segmentation methods.\",\"PeriodicalId\":6447,\"journal\":{\"name\":\"2009 IEEE Symposium on Industrial Electronics & Applications\",\"volume\":\"1 1\",\"pages\":\"6-10\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Symposium on Industrial Electronics & Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIEA.2009.5356492\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Symposium on Industrial Electronics & Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIEA.2009.5356492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

由于散斑噪声的干扰和边界的模糊性,超声图像分割具有挑战性。本文提出了一种基于图像分解的空间信息模糊c均值聚类分割方案。首先,将超声图像分解为u+v两个函数的和,其中u表示图像强度,v表示纹理。然后,对图像强度分量采用空间FCM聚类方法进行分割。在模拟和临床超声图像的实验中,与其他预处理或分割方法相比,该方法可以得到更准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image decomposition based ultrasound image segmentation by using fuzzy clustering
Ultrasound image segmentation is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, we propose a segmentation scheme using fuzzy c-means (FCM) clustering incorporating spatial information based on image decomposition. First, an ultrasound image is decomposed into a sum of two functions, u+v, where u denotes the image intensity while v refers to the texture. And then, a spatial FCM clustering method is applied on the image intensity component for segmentation. In the experiments with simulated and clinical ultrasound images, the proposed method can get more accurate results than other preprocessing or 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