{"title":"基于自适应模糊c均值技术的新型胸部CT图像肺结节计算机辅助检测系统","authors":"Jinke Wang, Yuanzhi Cheng","doi":"10.1109/IHMSC.2015.30","DOIUrl":null,"url":null,"abstract":"This paper presents a new pulmonary nodules computer-aided detection system in chest CT images utilizing the adaptive fuzzy C-Means (AFCM) technologies. Since rough segmentation of nodules tends to result in high false positive (FP), the main purpose of this study is to reduce the false-positive of candidate nodules via the clustering and classifying approaches. The proposed scheme consists of three phases: pulmonary nodule identification, training nodules clustering, and testing nodules classification. Firstly, the lung parenchyma is extracted through neighborhood connected technology and masking processing, and by appropriate thresholding processing, the candidate nodules are identified. Then, for improving the performance in the training phase, we utilize the AFCM technology. Finally, the category of each testing candidate nodule is determined by Mahalanobis distance. We validated our method on 35 volumes of chest CT, which is subdivided into 20 training part and 15 testing part, and an approximate false-positive of 2.8 per scan is obtained in our experiment. The preliminary results prove that our scheme is a promising tool for pulmonary nodule detection.","PeriodicalId":6592,"journal":{"name":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"41 1","pages":"514-517"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A New Pulmonary Nodules Computer-Aided Detection System in Chest CT Images Based on Adaptive Fuzzy C-Means Technology\",\"authors\":\"Jinke Wang, Yuanzhi Cheng\",\"doi\":\"10.1109/IHMSC.2015.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new pulmonary nodules computer-aided detection system in chest CT images utilizing the adaptive fuzzy C-Means (AFCM) technologies. Since rough segmentation of nodules tends to result in high false positive (FP), the main purpose of this study is to reduce the false-positive of candidate nodules via the clustering and classifying approaches. The proposed scheme consists of three phases: pulmonary nodule identification, training nodules clustering, and testing nodules classification. Firstly, the lung parenchyma is extracted through neighborhood connected technology and masking processing, and by appropriate thresholding processing, the candidate nodules are identified. Then, for improving the performance in the training phase, we utilize the AFCM technology. Finally, the category of each testing candidate nodule is determined by Mahalanobis distance. We validated our method on 35 volumes of chest CT, which is subdivided into 20 training part and 15 testing part, and an approximate false-positive of 2.8 per scan is obtained in our experiment. The preliminary results prove that our scheme is a promising tool for pulmonary nodule detection.\",\"PeriodicalId\":6592,\"journal\":{\"name\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"41 1\",\"pages\":\"514-517\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2015.30\",\"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 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2015.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Pulmonary Nodules Computer-Aided Detection System in Chest CT Images Based on Adaptive Fuzzy C-Means Technology
This paper presents a new pulmonary nodules computer-aided detection system in chest CT images utilizing the adaptive fuzzy C-Means (AFCM) technologies. Since rough segmentation of nodules tends to result in high false positive (FP), the main purpose of this study is to reduce the false-positive of candidate nodules via the clustering and classifying approaches. The proposed scheme consists of three phases: pulmonary nodule identification, training nodules clustering, and testing nodules classification. Firstly, the lung parenchyma is extracted through neighborhood connected technology and masking processing, and by appropriate thresholding processing, the candidate nodules are identified. Then, for improving the performance in the training phase, we utilize the AFCM technology. Finally, the category of each testing candidate nodule is determined by Mahalanobis distance. We validated our method on 35 volumes of chest CT, which is subdivided into 20 training part and 15 testing part, and an approximate false-positive of 2.8 per scan is obtained in our experiment. The preliminary results prove that our scheme is a promising tool for pulmonary nodule detection.