Open AccessShape and appearance priors for level set‐based left ventricle segmentation
Author(s) -
Yang Ronghua,
Mirmehdi Majid,
Xie Xianghua,
Hall David
Publication year - 2013
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.2012.0081
Subject(s) - constraint (computer aided design) , segmentation , artificial intelligence , level set (data structures) , computer science , maximum a posteriori estimation , prior probability , probabilistic logic , a priori and a posteriori , computer vision , flexibility (engineering) , pattern recognition (psychology) , image segmentation , active appearance model , mathematics , bayesian probability , image (mathematics) , maximum likelihood , geometry , philosophy , statistics , epistemology
The authors propose a novel spatiotemporal constraint based on shape and appearance and combine it with a level‐set deformable model for left ventricle (LV) segmentation in four‐dimensional gated cardiac SPECT, particularly in the presence of perfusion defects. The model incorporates appearance and shape information into a ‘soft‐to‐hard’ probabilistic constraint, and utilises spatiotemporal regularisation via a maximum a posteriori framework. This constraint force allows more flexibility than the rigid forces of shape constraint‐only schemes, as well as other state of the art joint shape and appearance constraints. The combined model can hypothesise defective LV borders based on prior knowledge. The authors present comparative results to illustrate the improvement gain. A brief defect detection example is finally presented as an application of the proposed method.