基于颜色指数局部自相关特征提取优化方法的病理图像计算癌检测

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI:10.1109/ISBI.2014.6867997

Jia Qu, H. Nosato, H. Sakanashi, E. Takahashi, Kensuke Terai, N. Hiruta

{"title":"基于颜色指数局部自相关特征提取优化方法的病理图像计算癌检测","authors":"Jia Qu, H. Nosato, H. Sakanashi, E. Takahashi, Kensuke Terai, N. Hiruta","doi":"10.1109/ISBI.2014.6867997","DOIUrl":null,"url":null,"abstract":"Aiming to lessen the burdens of the pathologist with efficient diagnosis assistance, this paper proposes a cancer detection method for pathological images utilizing color features based on color-index local auto-correlations (CILAC), applied to color-indexed images to utilize co-occurrence information about indexed pixels. Moreover, a method for the automatic optimization of feature extraction is also proposed. Based on a database including both benign and cancerous pathological images, experimental results show enhanced performance compared to prior research, which demonstrate the effectiveness of the proposed cancer detection method.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Computational cancer detection of pathological images based on an optimization method for color-index local auto-correlation feature extraction\",\"authors\":\"Jia Qu, H. Nosato, H. Sakanashi, E. Takahashi, Kensuke Terai, N. Hiruta\",\"doi\":\"10.1109/ISBI.2014.6867997\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming to lessen the burdens of the pathologist with efficient diagnosis assistance, this paper proposes a cancer detection method for pathological images utilizing color features based on color-index local auto-correlations (CILAC), applied to color-indexed images to utilize co-occurrence information about indexed pixels. Moreover, a method for the automatic optimization of feature extraction is also proposed. Based on a database including both benign and cancerous pathological images, experimental results show enhanced performance compared to prior research, which demonstrate the effectiveness of the proposed cancer detection method.\",\"PeriodicalId\":440405,\"journal\":{\"name\":\"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)\",\"volume\":\"10 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBI.2014.6867997\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2014.6867997","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

为了减轻病理医师的诊断负担，提供有效的诊断辅助，本文提出了一种基于颜色索引局部自相关(CILAC)的病理图像癌症检测方法，并将其应用于颜色索引图像，利用索引像素的共现信息。此外，还提出了一种特征提取的自动优化方法。基于包含良性和癌性病理图像的数据库，实验结果表明，与先前的研究相比，该方法的性能有所提高，证明了所提出的癌症检测方法的有效性。

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

Computational cancer detection of pathological images based on an optimization method for color-index local auto-correlation feature extraction

Aiming to lessen the burdens of the pathologist with efficient diagnosis assistance, this paper proposes a cancer detection method for pathological images utilizing color features based on color-index local auto-correlations (CILAC), applied to color-indexed images to utilize co-occurrence information about indexed pixels. Moreover, a method for the automatic optimization of feature extraction is also proposed. Based on a database including both benign and cancerous pathological images, experimental results show enhanced performance compared to prior research, which demonstrate the effectiveness of the proposed cancer detection method.

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

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

自引率

0.00%

发文量