Abstract—Breast cancer is one of the leading causes of cancer death among women. As such, the role of digital mammographic screening is to detect cancerous lesions, at an early stage, and to provide high accuracy in the analysis of the size, shape, and location of abnormalities. Segmentation is arguably one of the most important aspects of a computer aided detection system, particularly for masses. This paper attempt to introduce two level set segmentation models for mass detection on digitized mammograms. The first in an edge-based level set algorithm, proposed by Osher and Sethian. The second is a region-based level set algorithm called the local Shan-Vese model. A comparative study will be given, in which we will assess the performance of the second approach in terms of efficiency.
Index Terms—breast segmentation, Level Set method, local chan-vese model, Osher and Sethian algorithm
Cite: Fouzia Boutaouche and Nacera Benamrane, "Mammographic Masses Segmentation Using Implicit Deformable Models: The LCV Model in Comparison with the Osher-Sethian Model," Journal of Image and Graphics, Vol. 2, No. 2, pp. 100-105, December 2014. doi: 10.12720/joig.2.2.100-105
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