Mr. Lei Xu Profile

Lei Xu

Ph.D Student at Univ at Buffalo

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Proceedings Article | 14 February 2012 Paper

Efficient searching of globally optimal and smooth multi-surfaces with shape priors

Branislav Stojkovic, Hu Ding, Milan Sonka, Jinhui Xu, Qi Song, Xiaodong Wu, Lei Xu

Proceedings Volume 8314, 83140N (2012) https://doi.org/10.1117/12.912316

KEYWORDS: Image segmentation, 3D image processing, Optical coherence tomography, Silicon, Medical imaging, Optimization (mathematics), Image processing, Computer engineering, Iterative methods, 3D modeling

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Despite extensive studies in the past, the problem of segmenting globally optimal multiple surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical iterative method based on a volumetric graph representation of the 3D image that incorporates curvature and shape prior information. Compared with the graph-based method, applying the shape prior to construct the graph on a specific preferred shape model allows easy incorporation of a wide spectrum of shape prior information. Furthermore, the key insight that computation of the objective function can be done independently in the x and y directions makes local improvement possible. Thus, instead of using global optimization technique such as maximum flow algorithm, the iteration based method is much faster. Additionally, the utilization of the curvature in the objective function ensures the smoothness. To the best of our knowledge, this is the first paper to combine the shape-prior penalties with utilizing curvature in objective function to ensure the smoothness of the generated surfaces while striving for achieving global optimality. To evaluate the performance of our method, we test it on a set of 14 3D OCT images. Comparing to the best existing approaches, our experiments suggest that the proposed method reduces the unsigned surface positioning errors form 5.44 ± 1.07(μm) to 4.52 ± 0.84(μm). Moreover, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images. The proposed method is also suitable for parallel implementation on GPUs, which could potentially allow us to segment highly noisy volumetric images in real time.

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