A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

Helder C. R. de Oliveira; Diego R. Moraes; Gustavo A. Reche; Lucas R. Borges; Juliana H. Catani; Nestor de Barros; Carlos F. E. Melo; Adilson Gonzaga; Marcelo A. C. Vieira

doi:10.1117/12.2255516

3 March 2017 A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

Helder C. R. de Oliveira, Diego R. Moraes, Gustavo A. Reche, Lucas R. Borges, Juliana H. Catani, Nestor de Barros, Carlos F. E. Melo, Adilson Gonzaga, Marcelo A. C. Vieira

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101342U (2017) https://doi.org/10.1117/12.2255516
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

Citation Download Citation

Helder C. R. de Oliveira, Diego R. Moraes, Gustavo A. Reche, Lucas R. Borges, Juliana H. Catani, Nestor de Barros, Carlos F. E. Melo, Adilson Gonzaga, and Marcelo A. C. Vieira "A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342U (3 March 2017); https://doi.org/10.1117/12.2255516

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