Feature-based differences between mammograms

Walter F. Good; Xiao Hui Wang; Glenn S. Maitz

doi:10.1117/12.483559

15 May 2003 Feature-based differences between mammograms

Walter F. Good, Xiao Hui Wang, Glenn S. Maitz

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.483559
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

A novel technique for assessing local and global differences between mammographic images was developed. This method uses correlations between abstract features extracted from corresponding views to compare image properties without resorting to processes that depend on exact geometrical congruence, such as image subtraction, which have a tendency to produce excessive artifact. The method begins by normalizing both digitized mammograms, after which a series of global and local feature filters are applied to each image. Each filter calculates values characterizing a particular property of the given image, and these values, for each property of interest are arranged in a feature vector. Corresponding elements in the two feature vectors are combined to produce a difference vector that indicates the change in the particular properties between images. Features are selected which are expected to be relatively invariant with respect to breast compression.

Citation Download Citation

Walter F. Good, Xiao Hui Wang, and Glenn S. Maitz "Feature-based differences between mammograms", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.483559

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