Nejla Jbeli, Rekka Mastouri, H. Neji, S. Hantous-Zannad, Nawrès Khlifa
{"title":"Detection and Characterization of Subsolid Juxta-pleural Lung Nodule from CT Images","authors":"Nejla Jbeli, Rekka Mastouri, H. Neji, S. Hantous-Zannad, Nawrès Khlifa","doi":"10.1109/CoDIT.2018.8394965","DOIUrl":null,"url":null,"abstract":"Lung cancer is the most frequent and lethal malignant tumor. Detection and characterization of pulmonary nodules in Computed Tomography images (CT) is a primordial task for lung cancer diagnosis at early stages. As juxta-pleural nodules are directly linked to the lung pleura, they contain an open contour which makes their extraction a challenging task. In this paper, we propose an automatic detection and classification method of a part-solid juxta-pleural nodule. The proposed method was tested on 18 CT scan images in axial acquisition and gave an accurate quantification of the solid component in a part-solid nodule.","PeriodicalId":128011,"journal":{"name":"2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)","volume":"9 9","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoDIT.2018.8394965","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Lung cancer is the most frequent and lethal malignant tumor. Detection and characterization of pulmonary nodules in Computed Tomography images (CT) is a primordial task for lung cancer diagnosis at early stages. As juxta-pleural nodules are directly linked to the lung pleura, they contain an open contour which makes their extraction a challenging task. In this paper, we propose an automatic detection and classification method of a part-solid juxta-pleural nodule. The proposed method was tested on 18 CT scan images in axial acquisition and gave an accurate quantification of the solid component in a part-solid nodule.