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Yu, L., Ma, F., Jayasuriya, A., Sigelle, M. and Perreau, S. (2007) A new contour detection approach in mammogram using rational wavelet filtering and MRF smoothing. Proceedings of 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, Glenelg, 3-5 December 2007, 106-111.

has been cited by the following article:

• TITLE: Extracting and smoothing contours in mammograms using Fourier descriptors

  AUTHORS: Cyrille K. Feudjio, Alain Tiedeu, Marie-Laure Noubeg, Mihaela Gordan, Aurel Vlaicu, Samuel Domngang

  KEYWORDS: Mammogram; Segmentation; Breast Contour; Smoothing; Fourier Descriptors

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.7 No.3, February 21, 2014

  ABSTRACT: Contour is an important pattern descriptor in image processing and particularly in region description, registration and length estimation. In many applications where contour is used, a good segmentation and an efficient smoothing method are needed. In X-ray images, such as mammograms, where object edge is not clearly discernible, estimating the object’s contour may yield substantial shift along the boundary due to noise or segmentation drawbacks. An appropriate smoothing is therefore required to reduce these effects. In this paper, an approach based on local adaptive threshold segmentation to extract contour and a new smoothing approach founded on Fourier descriptors are introduced. The experimental results of extraction obtained from a set of mammograms and compared with the breast regions delineated by radiologists yielded a percent overlap area of 98.7% ± 0.9% with false positive and negative rates of 0.36 ± 0.74 and 0.93 ± 0.44 respectively. The proposed method was tested on a set of images and improved the accuracy, leading to an average error of less than one pixel.