Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging |
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Authors: | X X Yin B W-H Ng Q Yang A Pitman K Ramamohanarao D Abbott |
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Institution: | (1) Centre for Biomedical Engineering, School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia;(2) Department of Computer Science and Software Engineering, The Melbourne School of Engineering, The University of Melbourne, Melbourne , VIC, 3010, Australia;(3) Apollo Medical Imaging Technology Pty. Ltd., North Melbourne, VIC, 3051, Australia;(4) Department of Anatomy and Cell Biology, The University of Melbourne and Sydney School of Medicine, The University of Notre Dame, Melbourne , Australia |
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Abstract: | In this article, we present a novel approach to localize anatomical features—breast costal cartilage—in dynamic contrast-enhanced
MRI using level sets. Current breast MRI diagnosis involves magnetic-resonance compatible needles for localization 12]. However, if the breast costal cartilage structure can be used as an alternative to the MR needle, this will not only assist
in avoiding invasive procedures, but will also facilitate monitoring of the movement of breasts caused by cardiac and respiratory
motion. This article represents a novel algorithm for achieving reliable detection and extraction of costal cartilage structures,
which can be used for the analysis of motion artifacts, with possible shape variations of the structure caused by uptake of
contrast agent, as well as a potential for the registration of breast. The algorithm represented in this article is to extract
volume features from post-contrast MR images at three different time slices for the analysis of motion artifacts, and we validate
the current algorithm according to the anatomic structure. This utilizes the level-set method 18] for the size selection of the region of interest. The variable shape of contours acquired from a level-set-based segment
image actually determines the feature region of interest, which is used as a guide to achieve initial masks for feature extraction.
Following this, the algorithm uses a K-means method for classification of the feature regions from other types of tissue and morphological operations with a choice
of an appropriate structuring element to achieve reliable masks and extraction of features. The segments of features can be
therefore obtained with the application of extracted masks for subsequent motion analysis of breast and for potential registration
purposes. |
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