access icon free Probabilistic shape-based segmentation method using level sets

© The Institution of Engineering and Technology
Received 01/12/2012, Accepted 12/09/2013, Revised 21/07/2013, Published 18/10/2013

In this study, a novel probabilistic, geometric and dynamic shape-based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two-dimensional principal component analysis method is applied on the training shapes to represent the shape variation with enough number of shape projections in the training step. The shape model is constructed using the implicit representation of the projected shapes. A new energy functional is proposed (i) to embed the shape model into the image domain and (ii) to estimate the shape coefficients. The proposed method is validated on synthetic and clinical images with various challenges such as the noise, occlusion and missing information. The authors compare their method with some of related works. Experiments show that the proposed segmentation method is more accurate and robust than other alternatives under different challenges.

* Note: Colour figures are available in the online version of this paper.

Inspec keywords: shape recognition; image representation; image segmentation; principal component analysis

Other keywords: energy functional; two-dimensional principal component analysis method; occlusion; image domain; geometric shape-based level sets method; synthetic images; image segmentation; projected shapes; clinical images; implicit representation; segmentation results; missing information; shape coefficients; level sets; probabilistic shape-based level sets method; dynamic shape-based level sets method; probabilistic shape-based segmentation method

Subjects: Other topics in statistics; Other topics in statistics; Optical, image and video signal processing; Computer vision and image processing techniques

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