Detection of architectural distortion in mammograms using phase portraits

Fabio J. Ayres; Rangaraj M. Rangayyan

doi:10.1117/12.530966

12 May 2004 Detection of architectural distortion in mammograms using phase portraits

Fabio J. Ayres, Rangaraj M. Rangayyan

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.530966
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Architectural distortion is a subtle abnormality in mammograms, and a source of overlooking errors by radiologists. Computer-aided diagnosis (CAD) techniques can improve the performance of radiologists in detecting masses and calcifications; however, most CAD systems have not been designed to detect architectural distortion. We present a new method to detect and localize architectural distortion by analyzing the oriented texture in mammograms. A bank of Gabor filters is used to obtain the orientation field of the given mammogram. The orientation field is filtered and downsampled, to reduce noise and also to reduce the computational effort required by the subsequent methods. The downsampled orientation field is analyzed to produce three phase portrait maps: node, saddle, and spiral. The node map is linearly filtered, thresholded, and morphologically filtered to detect architectural distortion. The method was tested with 18 mammograms containing architectural distortion. In a preliminary experiment, a sensitivity of 88% was obtained at 15 false positives per image. Several possibilities for the improvement of the technique are being explored. A qualitative analysis of the performance of the method with stellate lesions indicates potential for enhancement of the technique.

Citation Download Citation

Fabio J. Ayres and Rangaraj M. Rangayyan "Detection of architectural distortion in mammograms using phase portraits", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.530966

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