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12 March 2010 Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method
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Abstract
In this paper, we present a shape based segmentation methodology for magnetic resonance prostate images. We first propose a new way to represent shapes via the hyperbolic tangent of the signed distance function. This effectively corrects the drawbacks of the signed distance function and yields very reasonable results for the shape registration and learning. Secondly, under a Bayesian statistical framework, instead of computing the posterior using a uniform prior, a directional distance map is introduced in order to incorporate a priori knowledge of image content as well as the estimated center of target object. Essentially, the image is modeled as a Finsler manifold and the metric is computed out of the directional derivative of the image. Then the directional distance map is computed to suppress the posterior remote from the object center. Thirdly, in the posterior image, a localized region based cost functional is designed to drive the shape based segmentation. Such cost functional utilizes the local regional information and is robust to both image noise and remote/irrelevant disturbances. With these three major components, the entire shape based segmentation procedure is provided as a complete open source pipeline and is applied to magnetic resonance image (MRI) prostate data.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yi Gao and Allen Tannenbaum "Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762315 (12 March 2010); https://doi.org/10.1117/12.843248
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