Paper Info
Title
A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images.
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%).
Year
DOI
Venue
2017
10.1117/12.2255516
Proceedings of SPIE
Keywords
Field
DocType
architectural distortion,mammography,local mapped pattern,local binary pattern,haralick,texture descriptor
Digital mammography,Mammography,Computer vision,Feature vector,Texture Descriptor,Pattern recognition,Local binary patterns,Digital image,Feature extraction,Artificial intelligence,Pixel,Physics
Conference
Volume
ISSN
Citations 
10134
0277-786X
2
PageRank 
References 
Authors
0.37
7
9
Name
Order
Citations
PageRank
Helder Cesar Rodigues de Oliveira122.39
Diego R. Moraes220.37
Gustavo A. Reche320.37
Lucas R. Borges474.26
Juliana H. Catani520.37
Nestor de Barros621.38
Carlos F. E. Melo721.04
Adilson Gonzaga88013.27
Marcelo A. C. Vieira9176.90