{"title":"利用空间关系分割肺区域的病灶检测","authors":"Donia Ben Hassen, H. Taleb","doi":"10.1109/ICITES.2012.6216669","DOIUrl":null,"url":null,"abstract":"In this paper, we have described a lesion detection approach from chest radiography. We have illustrated the importance of accurate segmentation as a preprocessing step in a CAD scheme. Then, a suitable combination among 118 features has been identified using the forward stepwise selection method. The main idea is to obtain a set of features that is enable a CAD not to discriminate between normal lesions and abnormal ones but to specify its nature if this lesion is an infection for example.","PeriodicalId":137864,"journal":{"name":"2012 International Conference on Information Technology and e-Services","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Lesion detection in lung regions that are segmented using spatial relations\",\"authors\":\"Donia Ben Hassen, H. Taleb\",\"doi\":\"10.1109/ICITES.2012.6216669\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we have described a lesion detection approach from chest radiography. We have illustrated the importance of accurate segmentation as a preprocessing step in a CAD scheme. Then, a suitable combination among 118 features has been identified using the forward stepwise selection method. The main idea is to obtain a set of features that is enable a CAD not to discriminate between normal lesions and abnormal ones but to specify its nature if this lesion is an infection for example.\",\"PeriodicalId\":137864,\"journal\":{\"name\":\"2012 International Conference on Information Technology and e-Services\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Information Technology and e-Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITES.2012.6216669\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Information Technology and e-Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITES.2012.6216669","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lesion detection in lung regions that are segmented using spatial relations
In this paper, we have described a lesion detection approach from chest radiography. We have illustrated the importance of accurate segmentation as a preprocessing step in a CAD scheme. Then, a suitable combination among 118 features has been identified using the forward stepwise selection method. The main idea is to obtain a set of features that is enable a CAD not to discriminate between normal lesions and abnormal ones but to specify its nature if this lesion is an infection for example.