Microcalcification texture analysis in a hybrid system for computer-aided mammography

Galina L. Rogova; Paul C. Stomper; Chih-Chung Ke

doi:10.1117/12.348542

21 May 1999 Microcalcification texture analysis in a hybrid system for computer-aided mammography

Galina L. Rogova, Paul C. Stomper, Chih-Chung Ke

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348542
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

Characterization of microcalcifications with high level of confidence is a very challenging problem since microcalcifications are very small and the difference between benign and malignant clusters is often very subtle. The overall goal of the presented research is to develop a hybrid evidential system for characterization of microcalcifications in order to provide radiologists with a computerized decision aid. The hybrid system intelligently combines a domain knowledge based subsystem with a computer vision subsystem to improve the confidence level of microcalcification characterization. This paper is mainly devoted to the description of the developed computer vision part of the hybrid system. The computer vision subsystem is represented by a hierarchical evidential classifier that computes evidences about the class membership of individual microcalcifications based on their texture and then uses these evidences in a neural network for clusters characterization. The texture of each individual classification is represented by two features: the fractal dimension and a four dimension vector defined by coefficients of the Gabor expansion of a microcalcification image. The results obtained in our experiment prove the feasibility of using this method in the hybrid system.

Citation Download Citation

Galina L. Rogova, Paul C. Stomper, and Chih-Chung Ke "Microcalcification texture analysis in a hybrid system for computer-aided mammography", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348542

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