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Characterizing Architectural Distortion in Mammograms by Linear Saliency

  • Systems-Level Quality Improvement
  • Published:
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Abstract

Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the complex superposition of breast tissue. This paper presents a novel AD characterization by representing the linear saliency in mammography Regions of Interest (ROI) as a graph composed of nodes corresponding to locations along the ROI boundary and edges with a weight proportional to the line intensity integrals along the path connecting any pair of nodes. A set of eigenvectors from the adjacency matrix is then used to extract discriminant coefficients that represent those nodes with higher salient lines. A dimensionality reduction is further accomplished by selecting the pair of nodes with major contribution for each of the computed eigenvectors. The set of main salient lines is then assembled as a feature vector that inputs a conventional Support Vector Machine (SVM). Experimental results with two benchmark databases, the mini-MIAS and DDSM databases, demonstrate that the proposed linear saliency domain method (LSD) performs well in terms of accuracy. The approach was evaluated with a set of 246 RoI extracted from the DDSM (123 normal tissues and 123 AD) and a set of 38 ROI from the mini-MIAS collections (19 normal tissues and 19 AD) respectively. The classification results showed respectively for both databases an accuracy rate of 89 % and 87 %, a sensitivity rate of 85 % and 95 %, and a specificity rate of 93 % and 84 %. Likewise, the area under curve (A z ) of the Receiver Operating Characteristic (ROC) curve was 0.93 for both databases.

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Notes

  1. http://marathon.csee.usf.edu/Mammography/Database.html.

  2. http://peipa.essex.ac.uk/info/mias.html.

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Acknowledgments

This work was partially supported by the Ecuadorian government through ”La Secretaría de Educación Superior, Ciencia, Tecnología e Innovacion (SENESCYT)”, [Grant number: 20110958, 2011].

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Authors and Affiliations

  1. Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia

    Fabián Narváez, Jorge Alvarez, Juan D. Garcia-Arteaga, Jonathan Tarquino & Eduardo Romero

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  1. Fabián Narváez
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  2. Jorge Alvarez
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  3. Juan D. Garcia-Arteaga
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Correspondence to Eduardo Romero.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Narváez, F., Alvarez, J., Garcia-Arteaga, J.D. et al. Characterizing Architectural Distortion in Mammograms by Linear Saliency. J Med Syst 41, 26 (2017). https://doi.org/10.1007/s10916-016-0672-5

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