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Proceedings Paper

Nonlinear enhancement of mammograms using area morphology
Author(s): Michael A. Wirth; Jennifer Lyon; Dennis Nikitenko
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Paper Abstract

The process of contrast enhancement refers to the accentuation, or sharpening of image structures to allow for improved image analysis and interpretation. A mammogram is a x-ray projection of the 3D structures of the breast obtained by compressing the breast between two plates. Unlike most other x-ray or Computed Tomography images, mammograms have an inherent "fuzzy" or diffuse appearance. This is due in part to the superimposition of densities from differing breast tissues, and the differential x-ray attenuation (absorption) characteristics associated with these various tissues. Much of the difficulty in accurately interpreting a mammogram is related to there being insufficient contrast to accurately identify potential abnormalities. Recently, morphology-based algorithms based on structuring elements have been proposed for contrast enhancement. One of the limitations of these approaches from traditional morphology is their dependence on the shape of structuring elements. In certain circumstances it may be more appropriate to filter an image using attributes of structures such as their size, irrespective of shape. This paper introduces a novel nonlinear enhancement technique that is based on the concept of area morphology. Various mammogram structures are enhanced to illustrate the technique and a comparison is made with enhancement techniques such as “Contrast Limited Adaptive Histogram Equalization” and classical morphological enhancement.

Paper Details

Date Published: 12 May 2004
PDF: 12 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535310
Show Author Affiliations
Michael A. Wirth, Univ. of Guelph (Canada)
Jennifer Lyon, Univ. of Guelph (Canada)
Dennis Nikitenko, Univ. of Guelph (Canada)


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

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