{"title":"Lungs Nodule Detection by Using Fuzzy Morphology from CT Scan Images","authors":"M. Jaffar, Ayyaz Hussain, A. M. Mirza","doi":"10.1109/IACSIT-SC.2009.89","DOIUrl":null,"url":null,"abstract":"In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) scanned images by using Fuzzy C-Mean (FCM) and morphological techniques. First of all, fuzzy have been used for automated segmentation of lungs. Region of interests (ROIs) have been extracted by using 8 directional searches slice by slice and then 3D ROI image have been constructed. A 3D template has been constructed and convolves with the 3D ROI image. Finally FCM have been used to extract ROI that contain nodule. The proposed system is capable to perform fully automatic segmentation and nodule detection from CT Scan Lungs images, based solely on information contained by the image itself. The technique was tested against the 50 datasets of different patients received from Aga Khan Medical University, Pakistan and Lung Image Database Consortium (LIDC) dataset.","PeriodicalId":286158,"journal":{"name":"2009 International Association of Computer Science and Information Technology - Spring Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Association of Computer Science and Information Technology - Spring Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IACSIT-SC.2009.89","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) scanned images by using Fuzzy C-Mean (FCM) and morphological techniques. First of all, fuzzy have been used for automated segmentation of lungs. Region of interests (ROIs) have been extracted by using 8 directional searches slice by slice and then 3D ROI image have been constructed. A 3D template has been constructed and convolves with the 3D ROI image. Finally FCM have been used to extract ROI that contain nodule. The proposed system is capable to perform fully automatic segmentation and nodule detection from CT Scan Lungs images, based solely on information contained by the image itself. The technique was tested against the 50 datasets of different patients received from Aga Khan Medical University, Pakistan and Lung Image Database Consortium (LIDC) dataset.