Automatic segmentation of cell nuclei using Krill Herd optimization based multi-thresholding and Localized Active Contour Model

2016 ◽  
Vol 36 (4) ◽  
pp. 584-596 ◽  
Author(s):  
Sabeena Beevi K. ◽  
Madhu S. Nair ◽  
G.R. Bindu
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Automatic Segmentation ◽  
Cell Nuclei ◽  
Contour Model ◽  
Krill Herd

scholarly journals A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Chao Ma ◽  
Gongning Luo ◽  
Kuanquan Wang
Keyword(s):  
Left Atrium ◽  
Random Forests ◽  
Active Contour ◽  
Active Contour Model ◽  
Automatic Segmentation ◽  
Image Resolution ◽  
Volumetric Mri ◽  
Contour Model ◽  
Fully Automatic ◽  
Magnetic Resonance Imaging Mri

Segmentation of the left atrium (LA) from cardiac magnetic resonance imaging (MRI) datasets is of great importance for image guided atrial fibrillation ablation, LA fibrosis quantification, and cardiac biophysical modelling. However, automated LA segmentation from cardiac MRI is challenging due to limited image resolution, considerable variability in anatomical structures across subjects, and dynamic motion of the heart. In this work, we propose a combined random forests (RFs) and active contour model (ACM) approach for fully automatic segmentation of the LA from cardiac volumetric MRI. Specifically, we employ the RFs within an autocontext scheme to effectively integrate contextual and appearance information from multisource images together for LA shape inferring. The inferred shape is then incorporated into a volume-scalable ACM for further improving the segmentation accuracy. We validated the proposed method on the cardiac volumetric MRI datasets from the STACOM 2013 and HVSMR 2016 databases and showed that it outperforms other latest automated LA segmentation methods. Validation metrics, average Dice coefficient (DC) and average surface-to-surface distance (S2S), were computed as0.9227±0.0598and1.14±1.205 mm, versus those of 0.6222–0.878 and 1.34–8.72 mm, obtained by other methods, respectively.

scholarly journals Generic and robust method for automatic segmentation of PET images using an active contour model

2016 ◽  
Vol 43 (8Part1) ◽  
pp. 4483-4494 ◽  
Author(s):  
Mingzan Zhuang ◽  
Rudi A. J. O. Dierckx ◽  
Habib Zaidi
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Automatic Segmentation ◽  
Robust Method ◽  
Contour Model
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