Prostate contours delineation using interactive directional active contours model and parametric shape prior model

2015 ◽  
Vol 31 (11) ◽  
pp. e02726 ◽  
Author(s):  
Foued Derraz ◽  
Gérard Forzy ◽  
Arnaud Delebarre ◽  
Abdelmalik Taleb-Ahmed ◽  
Mourad Oussalah ◽  
...  
Keyword(s):  
Active Contours ◽  
Shape Prior ◽  
Prior Model

A simple shape prior model for iris image segmentation

2011 ◽  
Author(s):  
Daniel A. Bishop ◽  
Anthony Yezzi, Jr.
Keyword(s):  
Image Segmentation ◽  
Shape Prior ◽  
Simple Shape ◽  
Prior Model ◽  
Iris Image

Free Registration Based Shape Prior for Active Contours

2019 ◽  
Author(s):  
Ines sakly ◽  
Mohamed Amine Mezghich ◽  
Slim M'hiri ◽  
Faouzi Ghorbel
Keyword(s):  
Active Contours ◽  
Shape Prior

scholarly journals Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
I. Cruz-Aceves ◽  
J. G. Avina-Cervantes ◽  
J. M. Lopez-Hernandez ◽  
M. G. Garcia-Hernandez ◽  
M. A. Ibarra-Manzano
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Human Heart ◽  
Active Contours ◽  
Particle Swarm ◽  
Clinical Decision ◽  
Magnetic Resonance Images ◽  
Polar Coordinate System ◽  
Shape Prior ◽  
Swarm Optimization

This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour. Moreover, to assess the performance of the cardiac medical image segmentations obtained by the proposed method and by the interactive techniques regarding the regions delineated by experts, a set of validation metrics has been adopted. The experimental results are promising and suggest that the proposed method is capable of segmenting human heart and ventricular areas accurately, which can significantly help cardiologists in clinical decision support.

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