{"title":"基于像素分割方法的计算机辅助乳房x线质量检测的多分辨率分析","authors":"J. Pragathi, H. Patil","doi":"10.1109/ICRTIT.2013.6844207","DOIUrl":null,"url":null,"abstract":"Mammography is an X-ray imaging technique for diagnosing breast tumour. Segmentation of tumour in the mammogram images are difficult task because they are poor in contrast and the lesions are surrounded by tissue with similar characteristics. In this paper, an automatic detection algorithm is proposed to segment the suspicious masses or lesions. Mammogram images are analyzed by wavelet and the algorithm utilizes combination of region based segmentation and pixel based segmentation to detect the masses. The performance of the system is then evaluated using a dataset containing 60 images. From the experimental results the relative error calculated for each image is less than 15% and exhibits a sensitivity of 90%.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multiresolution analysis for computer-aided mass detection in mammogram using pixel based segmentation method\",\"authors\":\"J. Pragathi, H. Patil\",\"doi\":\"10.1109/ICRTIT.2013.6844207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mammography is an X-ray imaging technique for diagnosing breast tumour. Segmentation of tumour in the mammogram images are difficult task because they are poor in contrast and the lesions are surrounded by tissue with similar characteristics. In this paper, an automatic detection algorithm is proposed to segment the suspicious masses or lesions. Mammogram images are analyzed by wavelet and the algorithm utilizes combination of region based segmentation and pixel based segmentation to detect the masses. The performance of the system is then evaluated using a dataset containing 60 images. From the experimental results the relative error calculated for each image is less than 15% and exhibits a sensitivity of 90%.\",\"PeriodicalId\":113531,\"journal\":{\"name\":\"2013 International Conference on Recent Trends in Information Technology (ICRTIT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Recent Trends in Information Technology (ICRTIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRTIT.2013.6844207\",\"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 International Conference on Recent Trends in Information Technology (ICRTIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRTIT.2013.6844207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiresolution analysis for computer-aided mass detection in mammogram using pixel based segmentation method
Mammography is an X-ray imaging technique for diagnosing breast tumour. Segmentation of tumour in the mammogram images are difficult task because they are poor in contrast and the lesions are surrounded by tissue with similar characteristics. In this paper, an automatic detection algorithm is proposed to segment the suspicious masses or lesions. Mammogram images are analyzed by wavelet and the algorithm utilizes combination of region based segmentation and pixel based segmentation to detect the masses. The performance of the system is then evaluated using a dataset containing 60 images. From the experimental results the relative error calculated for each image is less than 15% and exhibits a sensitivity of 90%.