MRI区域增长分割-一种度量拓扑方法

2015 IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS) Pub Date : 2015-02-01 DOI:10.1109/NCCCIS.2015.7295907

B. Gowri, G. Ilango

{"title":"MRI区域增长分割-一种度量拓扑方法","authors":"B. Gowri, G. Ilango","doi":"10.1109/NCCCIS.2015.7295907","DOIUrl":null,"url":null,"abstract":"Region growing technique has gained a significant importance in medical image segmentation for finer segmentation of tumor. Here, a novel region growing segmentation algorithm is proposed based on metric topological neighbourhoods. The quality of segmentation is measured using the new objective measure entropy along with the traditional validity measures accuracy, PSNR and MSE.","PeriodicalId":201980,"journal":{"name":"2015 IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS)","volume":"415 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Region growing segmentation of MRI — A metric topological approach\",\"authors\":\"B. Gowri, G. Ilango\",\"doi\":\"10.1109/NCCCIS.2015.7295907\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Region growing technique has gained a significant importance in medical image segmentation for finer segmentation of tumor. Here, a novel region growing segmentation algorithm is proposed based on metric topological neighbourhoods. The quality of segmentation is measured using the new objective measure entropy along with the traditional validity measures accuracy, PSNR and MSE.\",\"PeriodicalId\":201980,\"journal\":{\"name\":\"2015 IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS)\",\"volume\":\"415 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NCCCIS.2015.7295907\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCCCIS.2015.7295907","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

区域生长技术在医学图像分割中具有重要的意义，可以对肿瘤进行更精细的分割。本文提出了一种基于度量拓扑邻域的区域增长分割算法。利用新的客观度量熵和传统的有效性度量精度、PSNR和MSE来度量分割的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Region growing segmentation of MRI — A metric topological approach

Region growing technique has gained a significant importance in medical image segmentation for finer segmentation of tumor. Here, a novel region growing segmentation algorithm is proposed based on metric topological neighbourhoods. The quality of segmentation is measured using the new objective measure entropy along with the traditional validity measures accuracy, PSNR and MSE.

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

2015 IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS)

自引率

0.00%

发文量