Combining texture features from the MLO and CC views for mammographic CADx

Shalini Gupta; David Zhang; Mehul P. Sampat; Mia K. Markey

doi:10.1117/12.657023

17 March 2006 Combining texture features from the MLO and CC views for mammographic CAD_x

Shalini Gupta, David Zhang, Mehul P. Sampat, Mia K. Markey

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61445V (2006) https://doi.org/10.1117/12.657023
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

The purpose of this study was to investigate approaches for combining information from the MLO and CC mammographic views for Computer-aided Diagnosis (CADx) algorithms. Feature level and classifier output level combinations were explored. Linear discriminant analysis (LDA) with step-wise feature selection from a set of Haralick's texture features was used to develop classifiers for distinguishing between benign and malignant mammographic lesions. The effect of correlation between features from the two views on the performance of classifiers was investigated. The single view models included: (a) an LDA model with stepwise selection based on the MLO view only (MLO-Only) and similarly (b) a CC-Only LDA model. The feature-level combination models included: (a) LDA based on concatenation of feature sets selected independently from the two views (FEAT_CON), (b) LDA based on the concatenated feature sets along with the corresponding value of each feature from the opposite view (FEAT_COR_CON) if the correlation was below a threshold, (c) LDA based on the average of the MLO and CC feature values (FEAT_AVG). The classifier output level combination models investigated included: (a) average of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_AVG), (b) maximum of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_MAX), (c) minimum of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_MIN), (d) a second level LDA classifier on the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_LDA), (e) product of the output values of the two classifiers (OUTPUT_PROD). The performance of the models was assessed and compared using the ROC methodology to determine if combination models performed better than the single-view models.

Citation Download Citation

Shalini Gupta, David Zhang, Mehul P. Sampat, and Mia K. Markey "Combining texture features from the MLO and CC views for mammographic CAD_x", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445V (17 March 2006); https://doi.org/10.1117/12.657023

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