{"title":"Segmentation of nuclei in digital pathology images","authors":"P. Guo, A. Evans, P. Bhattacharya","doi":"10.1109/ICCI-CC.2016.7862091","DOIUrl":null,"url":null,"abstract":"There are challenges for image cancer nuclei segmentation in clinical decision support systems for brain tumor diagnosis. In this study, we propose a method for segmentation of cancer nuclei when such conflicts of cancer nuclei involve ‘omics’ indicative of brain tumors pathologically. To constrain the problem space in the region of color information (i.e. cancer nuclei), we begin by converting the images into the V component of HSV (Hue, Saturation, Value) using the level-set segmentation (VLS) in the training stage, follow by applying the sparsity representation (SR) in the test stage. Via the SR, the proposed VLS-SR would exhibits an improved capability of searching recursively for the optimal threshold level-set in the working subsets of the SR for image cancer nuclei segmentation.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI-CC.2016.7862091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
There are challenges for image cancer nuclei segmentation in clinical decision support systems for brain tumor diagnosis. In this study, we propose a method for segmentation of cancer nuclei when such conflicts of cancer nuclei involve ‘omics’ indicative of brain tumors pathologically. To constrain the problem space in the region of color information (i.e. cancer nuclei), we begin by converting the images into the V component of HSV (Hue, Saturation, Value) using the level-set segmentation (VLS) in the training stage, follow by applying the sparsity representation (SR) in the test stage. Via the SR, the proposed VLS-SR would exhibits an improved capability of searching recursively for the optimal threshold level-set in the working subsets of the SR for image cancer nuclei segmentation.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
