{"title":"CT图像上肺结节的自动检测与分割","authors":"Chun-Shui Yang, Yuanvuan Wang, Yi Guo","doi":"10.1109/CISP-BMEI.2018.8633101","DOIUrl":null,"url":null,"abstract":"Lung nodule detection and segmentation is important for clinical diagnosis. This paper proposes a lung nodule detection and segmentation method based on a fully convolutional network (FCN), the level set method and other image processing techniques. Firstly, lung CT images are put into the FCN for lung segmentation. Secondly, lung nodules are detected inside the lung area using the threshold method and other image processing techniques. Finally, the detected lung nodules and their spiculation are segmented by the level set method and threshold method based on the coordinate system transformation. Experimental result shows that the proposed method can effectively detect and segment lung nodules with the detection accuracy of 100% and the dice overlap index of segmentation of 0.9. Therefore, this method can provide helpful references for clinical diagnosis on lung cancers.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"46 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Automatic Detection and Segmentation of Lung Nodule on CT Images\",\"authors\":\"Chun-Shui Yang, Yuanvuan Wang, Yi Guo\",\"doi\":\"10.1109/CISP-BMEI.2018.8633101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lung nodule detection and segmentation is important for clinical diagnosis. This paper proposes a lung nodule detection and segmentation method based on a fully convolutional network (FCN), the level set method and other image processing techniques. Firstly, lung CT images are put into the FCN for lung segmentation. Secondly, lung nodules are detected inside the lung area using the threshold method and other image processing techniques. Finally, the detected lung nodules and their spiculation are segmented by the level set method and threshold method based on the coordinate system transformation. Experimental result shows that the proposed method can effectively detect and segment lung nodules with the detection accuracy of 100% and the dice overlap index of segmentation of 0.9. Therefore, this method can provide helpful references for clinical diagnosis on lung cancers.\",\"PeriodicalId\":6474,\"journal\":{\"name\":\"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)\",\"volume\":\"46 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISP-BMEI.2018.8633101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP-BMEI.2018.8633101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Detection and Segmentation of Lung Nodule on CT Images
Lung nodule detection and segmentation is important for clinical diagnosis. This paper proposes a lung nodule detection and segmentation method based on a fully convolutional network (FCN), the level set method and other image processing techniques. Firstly, lung CT images are put into the FCN for lung segmentation. Secondly, lung nodules are detected inside the lung area using the threshold method and other image processing techniques. Finally, the detected lung nodules and their spiculation are segmented by the level set method and threshold method based on the coordinate system transformation. Experimental result shows that the proposed method can effectively detect and segment lung nodules with the detection accuracy of 100% and the dice overlap index of segmentation of 0.9. Therefore, this method can provide helpful references for clinical diagnosis on lung cancers.