Abstract #2482
Semi-automatic Prostate Segmentation via a Hidden Markov Model with Anatomical and Textural Priors
Christian Scharfenberger 1 , Dorothy Lui 1 , Farzad Khalvati 2 , Alexander Wong 1 , and Masoom Haider 2,3
1
Systems Design Engineering, University of
Waterloo, Waterloo, Ontario, Canada,
2
Department
of Medical Imaging, University of Toronto, Toronto,
Ontario, Canada,
3
Sunnybrook
Health Sciences Centre, Toronto, Ontario, Canada
The contouring and segmentation of the prostate gland is
an important task in computer-aided prostate cancer
screening using MRI. To assist medical professionals
with the segmentation process, we propose a novel
user-guided approach to prostate segmentation in MR
images. The approach optimizes the energy components of
a modified Decoupled Active Contour framework based on a
Hidden Markov Model and a Rician likelihood to
explicitly consider user guidance and textural and
anatomical priors. Extensive experiments based on 10
patient cases and a variety of evaluation metrics showed
that our approach provides a significant improvement
over an existing semi-automatic segmentation approach.
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