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J Med Imaging (Bellingham). 2016 Apr;3(2):027002. doi: 10.1117/1.JMI.3.2.027002. Epub 2016 Apr 25.

Automated quality assessment in three-dimensional breast ultrasound images.

Author information

1
mediri GmbH , Vangerowstr. 18, Heidelberg 69115, Germany.
2
Tohoku University , Tokuyama Laboratory, 6-3-09 Aramaki-Aoba Aoba-ku, Sendai 980-8579, Japan.
3
University of Girona , Campus Montilivi, Ed. P-IV, Girona 17071, Spain.
4
Radboud University Medical Center , Geert Grooteplein Zuid 10, Nijmegen 6525 GA, The Netherlands.
5
University libre de Bruxelles , Franklin Rooseveltlaan 50, Brussels 1050, Belgium.
6
mediri GmbH, Vangerowstr. 18, Heidelberg 69115, Germany; Fraunhofer MEVIS, Universitätsallee 29, Bremen 28359, Germany.

Abstract

Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspects.

KEYWORDS:

automated breast ultrasound imaging; image processing; image quality; machine learning

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