
Proceedings Paper
Efficient convex optimization-based curvature dependent contour evolution approach for medical image segmentationFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
Markov random field (MRF) based approaches are extensively used in image segmentation applications, which
often produce segmentation results with a boundary of minimal length/surface and tending to pass along image
edges, yet affected by boundary shrinkage or bias in the absence of proper image edge information to drive the
segmentation. In this paper, we propose a novel curvature re-weighted boundary smoothing term and introduce a
new convex optimization-based contour/surface evolution method for medical image segmentation. The proposed
curvature-based term generates the optimal solution with low curvatures and helps to avoid boundary shrinkage
and bias. This is particularly useful for segmenting medical images, in which noisy and poor image quality
exists widely and the shapes of anatomical objects are often smooth and even convex. Moreover, a new convex
optimization-based contour evolution method is applied to propagate the initial contour to the object of interest
efficiently and robustly. Distinct from the traditional methods for contour evolution, the proposed algorithm
provides a fully time-implicit contour evolution scheme, which allows a large evolution step-size to significantly
speed up convergence. It also propagates the contour to its globally optimal position during each discrete
time-frame, which improves the algorithmic robustness to noise and poor initialization. The fast continuous
max-flow-based algorithm for contour evolution is implemented on a commercially available graphics processing
unit (GPU) to achieve a high computational performance. Experimental results for both synthetic and 2D/3D
medical images showed that the proposed approach generated segmentation results efficiently and increased the
accuracy and robustness of segmentation by avoiding segmentation shrinkage and bias.
Paper Details
Date Published: 13 March 2013
PDF: 9 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866902 (13 March 2013); doi: 10.1117/12.2006313
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
PDF: 9 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866902 (13 March 2013); doi: 10.1117/12.2006313
Show Author Affiliations
Eranga Ukwatta, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Jing Yuan, The Univ. of Western Ontario (Canada)
Wu Qiu, The Univ. of Western Ontario (Canada)
The Univ. of Western Ontario (Canada)
Jing Yuan, The Univ. of Western Ontario (Canada)
Wu Qiu, The Univ. of Western Ontario (Canada)
Martin Rajchl, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Aaron Fenster, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
The Univ. of Western Ontario (Canada)
Aaron Fenster, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
© SPIE. Terms of Use
