{"title":"利用改进的模糊c均值聚类算法对多色荧光原位杂交(M-FISH)图像进行分割,同时结合了空间和光谱信息","authors":"Jingyao Li, D. Lin, Yu-ping Wang","doi":"10.1109/BIBM.2015.7359717","DOIUrl":null,"url":null,"abstract":"Multicolor Fluorescence In-Situ Hybridization (M-FISH) is an imaging technique for rapid detection of chromosomal abnormalities, where the segmentation of chromosomes has been a challenge. Multi-channel information of M-FISH images can be used in a segmentation algorithm to exploit the correlated information across channels for better image segmentation. In addition, the neighboring pixels share similar characteristics, so this spatial information can be further utilized to improve the robustness of the algorithm to the noise. Motivated by this fact, in this paper we proposed an improved Fuzzy C-means (FCM) clustering algorithm to overcome the problems of conventional FCM such as the sensitivity to noise by incorporating both spatial and spectral information. The experimental results on both simulated and real M-FISH images have shown that our proposed method can result in higher segmentation accuracy and lower false ratio than both conventional FCM and the improved adaptive FCM (IAFCM) we recently proposed.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Segmentation of Multicolor Fluorescence In-Situ Hybridization (M-FISH) image using an improved Fuzzy C-means clustering algorithm while incorporating both spatial and spectral information\",\"authors\":\"Jingyao Li, D. Lin, Yu-ping Wang\",\"doi\":\"10.1109/BIBM.2015.7359717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multicolor Fluorescence In-Situ Hybridization (M-FISH) is an imaging technique for rapid detection of chromosomal abnormalities, where the segmentation of chromosomes has been a challenge. Multi-channel information of M-FISH images can be used in a segmentation algorithm to exploit the correlated information across channels for better image segmentation. In addition, the neighboring pixels share similar characteristics, so this spatial information can be further utilized to improve the robustness of the algorithm to the noise. Motivated by this fact, in this paper we proposed an improved Fuzzy C-means (FCM) clustering algorithm to overcome the problems of conventional FCM such as the sensitivity to noise by incorporating both spatial and spectral information. The experimental results on both simulated and real M-FISH images have shown that our proposed method can result in higher segmentation accuracy and lower false ratio than both conventional FCM and the improved adaptive FCM (IAFCM) we recently proposed.\",\"PeriodicalId\":186217,\"journal\":{\"name\":\"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBM.2015.7359717\",\"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 International Conference on Bioinformatics and Biomedicine (BIBM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM.2015.7359717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Segmentation of Multicolor Fluorescence In-Situ Hybridization (M-FISH) image using an improved Fuzzy C-means clustering algorithm while incorporating both spatial and spectral information
Multicolor Fluorescence In-Situ Hybridization (M-FISH) is an imaging technique for rapid detection of chromosomal abnormalities, where the segmentation of chromosomes has been a challenge. Multi-channel information of M-FISH images can be used in a segmentation algorithm to exploit the correlated information across channels for better image segmentation. In addition, the neighboring pixels share similar characteristics, so this spatial information can be further utilized to improve the robustness of the algorithm to the noise. Motivated by this fact, in this paper we proposed an improved Fuzzy C-means (FCM) clustering algorithm to overcome the problems of conventional FCM such as the sensitivity to noise by incorporating both spatial and spectral information. The experimental results on both simulated and real M-FISH images have shown that our proposed method can result in higher segmentation accuracy and lower false ratio than both conventional FCM and the improved adaptive FCM (IAFCM) we recently proposed.