Paper
13 July 2022 Simultaneous pectoral muscle and nipple location in MLO mammograms, considering image quality assumptions
Author Affiliations +
Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860E (2022) https://doi.org/10.1117/12.2625778
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium
Abstract
Feature-based registration algorithms can be used to establish spatial correspondence between two image. Therefore, anatomical landmarks such as the breast boundary, pectoral muscle, nipple, duct and vessels need to be considered. The aim of this paper is to introduce a new approach which combine the pectoral muscle segmentation and nipple location, considering mammography quality assumptions. Pectoral muscle is initialized as a straight line from the top of the image to the nipple level. Afterwards, both pectoral muscle boundary and nipple position are optimized using an iterative approach. The results show that the nipple is localized on the contour of the corresponding area (error smaller than 10 mm) while the Dice’s coefficient of the pectoral muscle segmentation is equal to 0.84 ± 0.12 using a straight line which is improved using a Chan-Vese active contour approach, reaching 0.87 ± 0.13. Our algorithm is easily generalized and portable to a different mammographic system since it barely depends on images statistics -i.e. pixel intensity values-, and is just based on geometrical considerations.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
E. García, R. Martí, J. Martí, J. del Riego, C. Aynes, A. Oliver, and O. Diaz "Simultaneous pectoral muscle and nipple location in MLO mammograms, considering image quality assumptions", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860E (13 July 2022); https://doi.org/10.1117/12.2625778
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KEYWORDS
Nipple

Breast

Image segmentation

Mammography

Image processing algorithms and systems

Image quality

Skin

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