Abstract
Certain kinds of abnormalities in x-ray mammograms are associated with specific anatomical structures – in particular, linear structures. This association can, in principle, be exploited to improve the specificity and sensitivity with which the abnormalities can be detected. We compare annotated and the automatic detection of the scale and orientation associated with linear structure in mammograms. We investigate methods of classifying the detected structures into anatomical classes (spicules, vessel, duct, fibrous tissue etc) from their cross-sectional profiles. Automatic (linear and non-linear) classification results are compared with expert annotations using receiver operating characteristic analysis. We show that useful discrimination between anatomical classes is achieved. Some of this relies on simple attributes such as the width and contrast of the profile, but there is also important information carried by the shape of the profile.
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Keywords
- Linear Structure
- Line Operator
- Digital Mammography
- Principal Component Analysis Model
- Mammographic Image
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1999 Springer-Verlag Berlin Heidelberg
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Zwiggelaar, R., Taylor, C.J., Boggis, C.R.M. (1999). Automatic Classification of Linear Structures in Mammographic Images. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_29
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DOI: https://doi.org/10.1007/10704282_29
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