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Medical Imaging 2016
October 20-21, 2016
Volume 5, Issue 5(Suppl) OMICS J Radiol
ISSN: 2167-7964 ROA, an open access journal
conferenceseries .com
October 20-21, 2016 Chicago, USA
International Conference on
Medical Imaging & Diagnosis
Barath Narayanan Narayanan et al., OMICS J Radiol 2016, 6:5(Suppl)
Feature selection using linear classifiers for computer aided detection of pulmonary nodules in CT
Barath Narayanan Narayanan, Russell C Hardie and Temesguen M Kebede
University of Dayton, USA
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 using linear classifier 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 and selection, and (v) candidate classification. In this paper, we focus on backend processor which
comprises of (iv) and (v). We study the performance of the FlyerScan by implementing various feature selection methods such
as sequential forward selection, sequential backward removal or implementation of these after holding onto certain robust
features. We also evaluate the tuning of Fischer linear discriminant classifier and study the impact of feature selection on it.
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 would be studied based on k-fold validation. This research will aid in improving the nodule detection rate in CT scans,
thereby enhancing a patient’s chance of survival.
Biography
Barath Narayanan Narayanan is currently a Graduate Teaching Assistant for the ECE department at University of Dayton. He has done his graduation from SRM
University, Chennai, India in 2012 with a Bachelor’s degree in Electrical and Electronics Engineering. He obtained his Master’s degree in Electrical Engineering
from University of Dayton in 2013. He is currently pursuing his PhD research in the field of Medical Image Analysis. His research focuses on "Computer Aided
Detection for identifying lung nodules on Computer Tomography and Chest Radiography". Early detection of such potential cancerous nodules could potentially
save people’s lives from lung cancer. His specific areas of interests include Pattern Recognition and Image processing. His Master's publication revolved around
the application of Super-Resolution for JPEG2000 compressed images which are utilized for Airborne Imaging.