False positive reduction in mammographic mass detection using local binary patterns

Authors:
Arnau Oliver;Xavier Lladó;Jordi Freixenet;Joan Martí
Affiliations:
Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain;Institute of Informatics and Applications, University of Girona, Girona, Spain
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

Citing 4
Cited 10

Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to the classification of mammographic masses and normal breast tissue

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Face Description with Local Binary Patterns: Application to Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Texture Classification in Lung CT Using Local Binary Patterns

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Local binary patterns variants as texture descriptors for medical image analysis

Artificial Intelligence in Medicine
An SVM confidence-based approach to medical image annotation

CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid with local binary patterns

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Combining different local binary pattern variants to boost performance

Expert Systems with Applications: An International Journal
A very high performing system to discriminate tissues in mammograms as benign and malignant

Expert Systems with Applications: An International Journal
Survey on LBP based texture descriptors for image classification

Expert Systems with Applications: An International Journal
Automatic polyp detection for wireless capsule endoscopy images

Expert Systems with Applications: An International Journal
A simple method for improving local binary patterns by considering non-uniform patterns

Pattern Recognition
A new GLLD operator for mass detection in digital mammograms

Journal of Biomedical Imaging - Special issue on Advanced Signal Processing Methods for Biomedical Imaging

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper we propose a new approach for false positive reduction in the field of mammographic mass detection. The goal is to distinguish between the true recognized masses and the ones which actually are normal parenchyma. Our proposal is based on Local Binary Patterns (LBP) for representing salient micro-patterns and preserving at the same time the spatial structure of the masses. Once the descriptors are extracted, Support Vector Machines (SVM) are used for classifying the detected masses. We test our proposal using a set of 1792 suspicious regions of interest extracted from the DDSM database. Exhaustive experiments illustrate that LBP features are effective and efficient for false positive reduction even at different mass sizes, a critical aspect in mass detection systems. Moreover, we compare our proposal with current methods showing that LBP obtains better performance.