A computer-aided diagnosis system to identify regions of pathologic change in temporal subtraction images of the chest

Charles Ho; Katherine Lee; Samuel G. Armato III

doi:10.1117/12.2082820

20 March 2015 A computer-aided diagnosis system to identify regions of pathologic change in temporal subtraction images of the chest

Charles Ho, Katherine Lee, Samuel G. Armato III

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94141L (2015) https://doi.org/10.1117/12.2082820
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Radiologists often compare sequential radiographs side-by-side in order to identify regions of change and evaluate the clinical significance of such regions. Some changes in pathology, however, may be overlooked or misinterpreted. For this reason, temporal subtraction (TS) images provide an important tool for enhanced visualization. Not all areas of “change” demonstrated on a TS image, however, are caused by pathology. The purpose of this study was to develop an automated computer-aided diagnosis (CAD) system to locate regions of change in TS images as well as to classify such regions as being true regions of pathologic change or false regions of change caused by misregistration artifacts. The dataset used in this study contained 120 images, on which an experienced radiologist outlined 74 regions of true pathologic change that were used as the gold standard. Through gray-level thresholding and initial false-positive reduction, an initial set of candidates was extracted and inputted into a classifier. A five-fold cross-validation method was employed to create training and testing groups. Both false-candidate regions as well as the gold-standard regions were used as training data. Of the three classifiers tested (support vector machine, logistic regression, and linear discriminant analysis), the logistic regression classifier performed the best with a sensitivity of 96% and specificity of 84%; receiver operating characteristic (ROC) analysis resulted in an area under the ROC curve of 0.94. These results show promise in the performance of the CAD system to detect regions of pathologic changes in TS images of the chest.

Citation Download Citation

Charles Ho, Katherine Lee, and Samuel G. Armato III "A computer-aided diagnosis system to identify regions of pathologic change in temporal subtraction images of the chest", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141L (20 March 2015); https://doi.org/10.1117/12.2082820

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Lung

Radiography

CAD systems

Computer aided diagnosis and therapy

Chest

Pathology

Computing systems

Show All Keywords

Keywords/Phrases

Search In:

Publication Years