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Proceedings Paper

Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis
Author(s): Tetsuko Ichikawa; Tomoko Matsubara; Takeshi Hara; Hiroshi Fujita; Tokiko Endo; Takuji Iwase D.V.M.
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Paper Abstract

As well as mass and microcalcification, architectural distortion is a very important finding for the early detection of breast cancer via mammograms, and such distortions can be classified into three typical types: spiculation, retraction, and distortion. The purpose of this work is to develop an automatic method for detecting areas of architectural distortion with spiculation. The suspect areas are detected by concentration indexes of line-structures extracted by using mean curvature. After that, discrimination analysis of nine features is employed for the classifications of true and false positives. The employed features are the size, the mean pixel value, the mean concentration index, the mean isotropic index, the contrast, and four other features based on the power spectrum. As a result of this work, the accuracy of the classification was 76% and the sensitivity was 80% with 0.9 false positives per image in our database in regard to spiculation. It was concluded that our method was effective in detectiong the area of architectural distortion; however, some architectural distortions were not detected accurately because of the size, the density, or the different appearance of the distorted areas.

Paper Details

Date Published: 12 May 2004
PDF: 6 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535116
Show Author Affiliations
Tetsuko Ichikawa, Gifu Univ. (Japan)
Tomoko Matsubara, Nagoya Bunri Univ. (Japan)
Takeshi Hara, Gifu Univ. (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)
Tokiko Endo, National Hospital of Nagoya (Japan)
Takuji Iwase D.V.M., Cancer Institute Hospital (Japan)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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