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Proceedings Paper

Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography
Author(s): Helder C. R. de Oliveira; Arianna Mencattini; Paola Casti; Eugenio Martinelli; Corrado di Natale; Juliana H. Catani; Nestor de Barros; Carlos F. E. Melo; Adilson Gonzaga; Marcelo A. C. Vieira
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Paper Abstract

This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. 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 automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.

Paper Details

Date Published: 27 February 2018
PDF: 10 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752P (27 February 2018); doi: 10.1117/12.2293388
Show Author Affiliations
Helder C. R. de Oliveira, Univ. de São Paulo (Brazil)
Arianna Mencattini, Univ. degli Studi di Roma "Tor Vergata" (Italy)
Paola Casti, Univ. degli Studi di Roma "Tor Vergata" (Italy)
Eugenio Martinelli, Univ. degli Studi di Roma "Tor Vergata" (Italy)
Corrado di Natale, Univ. degli Studi di Roma "Tor Vergata" (Italy)
Juliana H. Catani, Univ. de São Paulo (Brazil)
Nestor de Barros, Univ. de São Paulo (Brazil)
Carlos F. E. Melo, Clínica Eco and Mama Diagnóstico Digital (Brazil)
Adilson Gonzaga, Univ. de São Paulo (Brazil)
Marcelo A. C. Vieira, Univ. de São Paulo (Brazil)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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