{"title":"基于连续约束的分层聚类图像分割","authors":"E. R. C. Morales, Yosu Yurramendi Mendizabal","doi":"10.1109/IPTA.2010.5586724","DOIUrl":null,"url":null,"abstract":"Traditional clustering methods do not take into account any relations possibly present in data. This paper introduces a contiguity-constrained algorithm with an aggregation index which uses neighbouring relations present in the data. Experiments show the behaviour of the proposed method in the case of medical image segmentation.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"45 13","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Contiguity-constrained hierarchical clustering for image segmentation\",\"authors\":\"E. R. C. Morales, Yosu Yurramendi Mendizabal\",\"doi\":\"10.1109/IPTA.2010.5586724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional clustering methods do not take into account any relations possibly present in data. This paper introduces a contiguity-constrained algorithm with an aggregation index which uses neighbouring relations present in the data. Experiments show the behaviour of the proposed method in the case of medical image segmentation.\",\"PeriodicalId\":236574,\"journal\":{\"name\":\"2010 2nd International Conference on Image Processing Theory, Tools and Applications\",\"volume\":\"45 13\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Conference on Image Processing Theory, Tools and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPTA.2010.5586724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPTA.2010.5586724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Contiguity-constrained hierarchical clustering for image segmentation
Traditional clustering methods do not take into account any relations possibly present in data. This paper introduces a contiguity-constrained algorithm with an aggregation index which uses neighbouring relations present in the data. Experiments show the behaviour of the proposed method in the case of medical image segmentation.
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