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.

De Oliveira, H., Mencattini, A., Casti, P., Martinelli, E., Di Natale, C., Catani, J.h., et al. (2018). Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (pp.96). SPIE [10.1117/12.2293388].

Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography

Mencattini A.;Casti P.;Martinelli E.;Di Natale C.;
2018-01-01

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.
Medical Imaging 2018: Computer-Aided Diagnosis
usa
2018
DECTRIS Ltd.
Rilevanza internazionale
2018
Settore ING-INF/07 - MISURE ELETTRICHE ED ELETTRONICHE
English
Architectural distortion
digital mammography
Gabor filter
local binary pattern
local mapped pattern
texture descriptor
Intervento a convegno
De Oliveira, H., Mencattini, A., Casti, P., Martinelli, E., Di Natale, C., Catani, J.h., et al. (2018). Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (pp.96). SPIE [10.1117/12.2293388].
De Oliveira, Hcr; Mencattini, A; Casti, P; Martinelli, E; Di Natale, C; Catani, Jh; De Barros, N; Melo, Cfe; Gonzaga, A; Vieira, Mac
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/265385
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