A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography
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ORIGINAL PAPER
A Novel Fusion‑Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography Osmando Pereira Junior1 · Helder Cesar Rodrigues Oliveira2 · Carolina Toledo Ferraz3 · José Hiroki Saito3 · Marcelo Andrade da Costa Vieira2 · Adilson Gonzaga2 Received: 30 July 2019 / Revised: 12 August 2020 / Accepted: 22 September 2020 © Society for Imaging Informatics in Medicine 2020
Abstract Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances. Keywords Architectural distortion · Fusion-based texture descriptor · Digital mammography · Fusion operators · Local texture descriptor * Osmando Pereira Junior [email protected] Helder Cesar Rodrigues Oliveira [email protected] Carolina Toledo Ferraz [email protected] José Hiroki Saito [email protected] Marcelo Andrade da Costa Vieira [email protected] Adilson Gonzaga [email protected] 1
Federal Institute of Education Science and Technology of Triângulo Mineiro (IFTM), Patrocínio, Minas Gerais, Brazil
2
São Carlos School of Engineering, University of São Paulo (EESC/USP), São Carlos, São Paulo, Brazil
3
University Center Campo Limpo Paulista (UNIFACCAMP), Campo Limpo Paulista (SP), São Paulo, Brazil
Introduction Breast cancer is one of the leading causes of death among women [1]. Early detection leads to better treatment options, increasing the chances of curing the disease [2, 3]. X-ray mammography is the most commonly used scre
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