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A Fast Algorithm for Geodesic Active Contours with Applications to Medical Image Segmentation
The geodesic active contour model (GAC) is a commonly used segmentation model
for medical image segmentation. The level set method (LSM) is the most popular
approach for solving the model, via implicitly representing the contour by a
level set function. However, the LSM suffers from high computation burden and
numerical instability, requiring additional regularization terms or
re-initialization techniques. In this paper, we use characteristic functions to
implicitly approximate the contours, propose a new representation to the GAC
and derive an efficient algorithm termed as the iterative
convolution-thresholding method (ICTM). Compared to the LSM, the ICTM is
simpler and much more efficient and stable. In addition, the ICTM enjoys most
desired features (e.g., topological changes) of the level set-based methods.
Extensive experiments, on 2D synthetic, 2D ultrasound, 3D CT, and 3D MR images
for nodule, organ and lesion segmentation, demonstrate that the ICTM not only
obtains comparable or even better segmentation results (compared to the LSM)
but also achieves dozens or hundreds of times acceleration.Comment: 10 page