Next Article in Journal
Developments in Electrical-Property Tomography Based on the Contrast-Source Inversion Method
Next Article in Special Issue
Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides
Previous Article in Journal
Origami Lesion-Targeting Device for CT-Guided Interventions
Previous Article in Special Issue
Macrosight: A Novel Framework to Analyze the Shape and Movement of Interacting Macrophages Using Matlab®
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
J. Imaging 2019, 5(2), 24;

Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in Annual Conference on Medical Image Understanding and Analysis, Southampton, UK, 9–11 July 2018.
Received: 30 November 2018 / Revised: 25 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Medical Image Understanding and Analysis 2018)
Full-Text   |   PDF [2343 KB, uploaded 1 February 2019]   |  


Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region. View Full-Text
Keywords: breast density classification; risk estimation; local binary patterns; texture descriptors breast density classification; risk estimation; local binary patterns; texture descriptors

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

George, M.; Zwiggelaar, R. Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring. J. Imaging 2019, 5, 24.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top