Proceedings of the 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS)
Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules. Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include (i) local contrast enhancement (ii) automated anatomical segmentation (iii) detection of potential nodule candidates (iv) feature computation & selection and (v) candidate classification. In this paper, we study the performance of the FlyerScan by implementing various classification methods such as linear, quadratic and Fischer linear discriminant classifier. This algorithm is implemented using a publicly available Lung Image Database Consortium – Image Database Resource Initiative (LIDC-IDRI) dataset. 107 cases from LIDC-IDRI are handpicked in particular for this paper and performance of the CAD system is studied based on 5 example cases of Automatic Nodule Detection (ANODE09) database. This research will aid in improving the nodule detection rate in CT scans, thereby enhancing a patient’s chance of survival.
Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Computed Tomography, Computer Aided Detection System, Lung Cancer, Fischer Linear Discriminant Classifier, Quadratic Classifier, Neural Network.
Narayanan, Barath Narayanan; Hardie, Russell C.; and Messay, Temesguen, "Analysis of Various Classification Techniques for Computer Aided Detection System of Pulmonary Nodules in CT" (2016). Electrical and Computer Engineering Faculty Publications. 409.
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons