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Proceedings Paper

Statistical modeling of lines and structures in mammograms
Author(s): Reyer Zwiggelaar; Tim C. Parr; Caroline R. M. Boggis; Susan M. Astley; Christopher J. Taylor
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Paper Abstract

Computer-aided prompting systems require the reliable detection of a variety of mammographic signs of cancer. The emphasis of the work described in this paper is the correct classification of linear structures in mammograms, especially those associated with spiculated lesions. The detection of spiculated lesions can be based on the detection of the radiating pattern of linear structures associated with these lesions. The accuracy of automated stellate lesion detection algorithms can be improved by differentiating between the linear structures associated with lesions and those occurring in normal tissue. Statistical modeling, based on principal component analysis (PCA), has been developed for describing the cross-sectional profiles of linear structures, the motivation being that the shapes of intensity profiles may be characteristic of the type of structure. For the detection of spiculated lesions the main interest is to classify the linear structures into two classes, spicules and non-spicules. PCA models have been applied to whole mammograms to determine the probability that a particular type of linear structure (e.g. a spicule in this case) is present at any given location in the image.

Paper Details

Date Published: 25 April 1997
PDF: 12 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274137
Show Author Affiliations
Reyer Zwiggelaar, Univ. of Manchester (United Kingdom)
Tim C. Parr, Univ. of Manchester (United Kingdom)
Caroline R. M. Boggis, Withington Hospital (United Kingdom)
Susan M. Astley, Univ. of Manchester (United Kingdom)
Christopher J. Taylor, Univ. of Manchester (United Kingdom)

Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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