Computer-aided detection of breast masses on full-field digital mammograms: false positive reduction using gradient field analysis

Jun Wei; Berkman Sahiner; Lubomir M. Hadjiiski; Heang-Ping Chan; Nicholas Petrick; Mark A. Helvie; Chuan Zhou; Zhanyu Ge

doi:10.1117/12.536126

12 May 2004 Computer-aided detection of breast masses on full-field digital mammograms: false positive reduction using gradient field analysis

Jun Wei, Berkman Sahiner, Lubomir M. Hadjiiski, Heang-Ping Chan, Nicholas Petrick, Mark A. Helvie, Chuan Zhou, Zhanyu Ge

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.536126
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Several full-field digital mammography (FFDM) systems have been approved for clinical applications. It is important to develop a CAD system that can easily be adapted to images acquired by FFDM systems from different manufacturers. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multi-resolution preprocessing scheme for image enhancement. Our CAD system performed prescreening to identify mass candidates, segmented the suspicious structures, extracted morphological and texture features, and then classified masses and normal tissue. In this study, we investigated the use of a two-stage gradient field analysis to identify suspicious masses, and the effectiveness of a new gradient field feature extracted from each suspicious object for false positive (FP) reduction. A data set of 104 cases with 243 images acquired with a GE FFDM system was collected. Most cases had two mammographic views, except for 12 cases that had three views and 1 case with only one view. The data set contained 106 masses. The true locations of the masses were identified by an experienced radiologist. Using free-response receiver operating characteristic (FROC) analysis, it was found that our CAD system achieved a cased-based sensitivity of 70%, 80%, and 88% at 0.8, 1.3, and 1.7 FP marks/image, respectively. The high performance indicated the usefulness of the new gradient field analysis method.

Citation Download Citation

Jun Wei, Berkman Sahiner, Lubomir M. Hadjiiski, Heang-Ping Chan, Nicholas Petrick, Mark A. Helvie, Chuan Zhou, and Zhanyu Ge "Computer-aided detection of breast masses on full-field digital mammograms: false positive reduction using gradient field analysis", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.536126

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