title: Modeling of the lung nodules for detection in LDCT scans.
abstract: A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.
authors.affiliation: Department of Electrical & Computer Eng., University of Louisville, USA. aafara02@louisville.edu
authors.forename: Amal
authors.initials: A
authors.lastname: Farag
authors.forename: Shireen
authors.initials: S
authors.lastname: Elhabian
authors.forename: James
authors.initials: J
authors.lastname: Graham
authors.forename: Aly
authors.initials: A
authors.lastname: Farag
authors.forename: Salwa
authors.initials: S
authors.lastname: Elshazly
authors.forename: Robert
authors.initials: R
authors.lastname: Falk
authors.forename: Hani
authors.initials: H
authors.lastname: Mahdi
authors.forename: Hossam
authors.initials: H
authors.lastname: Abdelmunim
authors.forename: Sahar
authors.initials: S
authors.lastname: Al-Ghaafary
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*