{"title":"基于数学形态学的自适应CT图像分割","authors":"T. Ji, P. Wu, Mengshi Li, H. Zheng","doi":"10.1109/CEEC.2013.6659452","DOIUrl":null,"url":null,"abstract":"This paper proposes a generic scheme for CT image segmentation, which is based on mathematical morphology. After converting raw CT data into a gray scale image, morphological reconstruction and filtering techniques are employed to process the image, aiming at the accurate identification of the target. Simulation studies on insect CT data demonstrate that the proposed scheme is able to segment the target from the background with the presence of noise.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptive CT image segmentation using mathematical morphology\",\"authors\":\"T. Ji, P. Wu, Mengshi Li, H. Zheng\",\"doi\":\"10.1109/CEEC.2013.6659452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a generic scheme for CT image segmentation, which is based on mathematical morphology. After converting raw CT data into a gray scale image, morphological reconstruction and filtering techniques are employed to process the image, aiming at the accurate identification of the target. Simulation studies on insect CT data demonstrate that the proposed scheme is able to segment the target from the background with the presence of noise.\",\"PeriodicalId\":309053,\"journal\":{\"name\":\"2013 5th Computer Science and Electronic Engineering Conference (CEEC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 5th Computer Science and Electronic Engineering Conference (CEEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEEC.2013.6659452\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEEC.2013.6659452","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive CT image segmentation using mathematical morphology
This paper proposes a generic scheme for CT image segmentation, which is based on mathematical morphology. After converting raw CT data into a gray scale image, morphological reconstruction and filtering techniques are employed to process the image, aiming at the accurate identification of the target. Simulation studies on insect CT data demonstrate that the proposed scheme is able to segment the target from the background with the presence of noise.
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