Modeling of the lung nodules for detection in LDCT scans
Farag, Amal; Elhabian, Shireen; Graham, James; Farag, Aly; Elshazly, Salwa; Falk, Robert; Mahdi, Hossam; Hossam El DIn Hassan Abdelmunim; Al-Ghaafary, Sahar;
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. © 2010 IEEE.
Other data
| Title | Modeling of the lung nodules for detection in LDCT scans | Authors | Farag, Amal; Elhabian, Shireen; Graham, James; Farag, Aly; Elshazly, Salwa; Falk, Robert; Mahdi, Hossam; Hossam El DIn Hassan Abdelmunim ; Al-Ghaafary, Sahar | Keywords | Active appearance;Data-driven nodule models;Nodule modeling;Sensitivity and specificity of CAD systems | Issue Date | 1-Dec-2010 | Conference | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Embc 10 | ISBN | [9781424441235] | ISSN | 2375-7477 | DOI | 10.1109/IEMBS.2010.5627446 | PubMed ID | 21096845 | Scopus ID | 2-s2.0-78650851527 |
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