Computer-aided diagnosis of lesions on multimodality images of the breast

Maryellen Lissak Giger; Zhimin Huo; Karla Horsch; Edward R. Hendrick; Luz A. Venta; Carl J. Vyborny; Ioana R. Bonta; Li Lan

doi:10.1117/12.431141

3 July 2001 Computer-aided diagnosis of lesions on multimodality images of the breast

Maryellen Lissak Giger, Zhimin Huo, Karla Horsch, Edward R. Hendrick, Luz A. Venta, Carl J. Vyborny, Ioana R. Bonta, Li Lan

Author Affiliations +

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431141
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

We have developed computerized methods for the analysis of lesions that combine results from different imaging modalities, in this case digitized mammograms and sonograms of the breast, for distinguishing between malignant and benign lesions. The computerized classification method -- applied here to mass lesions seen on both digitized mammograms and sonograms, includes: (1) automatic lesion extraction, (2) automated feature extraction, and (3) automatic classification. The results for both modalities are then merged into an estimate of the likelihood of malignancy. For the mammograms, computer-extracted lesion features include degree of spiculation, margin sharpness, lesion density, and lesion texture. For the ultrasound images, lesion features include margin definition, texture, shape, and posterior acoustic attenuation. Malignant and benign lesions are better distinguished when features from both mammograms and ultrasound images are combined.

Citation Download Citation

Maryellen Lissak Giger, Zhimin Huo, Karla Horsch, Edward R. Hendrick, Luz A. Venta, Carl J. Vyborny, Ioana R. Bonta, and Li Lan "Computer-aided diagnosis of lesions on multimodality images of the breast", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431141

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