Open AccessProbabilistic shape‐based segmentation method using level sets
Author(s) -
Aslan Melih S.,
Shalaby Ahmed,
Abdelmunim Hossam,
Farag Aly A.
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0226
Subject(s) - active shape model , artificial intelligence , segmentation , probabilistic logic , computer science , shape analysis (program analysis) , pattern recognition (psychology) , principal component analysis , image segmentation , representation (politics) , noise (video) , point distribution model , computer vision , mathematics , image (mathematics) , static analysis , politics , political science , law , programming language
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.