A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography.
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-...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 1; pp. 36 - 53 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148753830&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148753830 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2021 vid: 34 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148753830 146945287 148753830 148753830 10.1007/s10278-020-00391-5 148753830 ppf: 36 ppct: 17 formats: fmt: @attributes: type: P tig: atl: A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography. aug: au: Junior, Osmando Pereira Oliveira, Helder Cesar Rodrigues Ferraz, Carolina Toledo Saito, José Hiroki Vieira, Marcelo Andrade da Costa Gonzaga, Adilson affil: Federal Institute of Education Science and Technology of Triângulo Mineiro (IFTM), Minas Gerais, Patrocínio, Brazil sug: subj: Breast Neoplasms Diagnosis Mammography Methods Early Detection of Cancer Methods Breast Pathology Radiographic Image Interpretation, Computer-Assisted Methods Human Diagnosis, Computer Assisted Methods Algorithms ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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