A novel automatically initialized level set approach based on region correlation for lumbar vertebrae CT image segmentation

2015 ◽  
Author(s):  
Yang Li ◽  
Wei Liang ◽  
Jindong Tan ◽  
Yinlong Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Lumbar Vertebrae ◽  
Ct Image ◽  
Level Set Approach

scholarly journals Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yang Li ◽  
Wei Liang ◽  
Yinlong Zhang ◽  
Jindong Tan
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Lumbar Vertebrae ◽  
Ct Image ◽  
Global Level ◽  
Level Set Function ◽  
Weak Boundary ◽  
Irregular Boundaries ◽  
Set Function ◽  
Level Set Approach

Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irregular boundaries (double boundary, weak boundary), and image noise. Therefore, this paper intentionally designed an automatic global level set approach (AGLSA), which is capable of dealing with these issues for lumbar vertebrae CT image segmentation. Unlike the traditional level set methods, we firstly propose an automatically initialized level set function (AILSF) that comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM) to automatically generate a smooth initial contour which is precisely adjacent to the object boundary. Secondly, a regularized level set formulation is introduced to overcome the weak boundary leaking problem, which utilizes the region correlation of histograms inside and outside the level set contour as a global term. Ultimately, a gradient vector flow (GVF) based edge-stopping function is employed to guarantee a fast convergence rate of the level set evolution and to avoid level set function oversegmentation at the same time. Our proposed approach has been tested on 115 vertebrae CT volumes of various patients. Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise.

scholarly journals A level-set approach to joint image segmentation and registration with application to CT lung imaging

2018 ◽  
Vol 65 ◽  
pp. 58-68 ◽  
Author(s):  
Piotr Swierczynski ◽  
Bartłomiej W. Papież ◽  
Julia A. Schnabel ◽  
Colin Macdonald
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Lung Imaging ◽  
Level Set Approach

Image segmentation using a multilayer level-set approach

2008 ◽  
Vol 12 (6) ◽  
pp. 267-285 ◽  
Author(s):  
Ginmo Chung ◽  
Luminita A. Vese
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Approach

Level Set Approach to Image Segmentation

2012 ◽  
Vol 2 (5) ◽  
pp. 110-113
Author(s):  
Bharatsinh P Solanki ◽  
◽  
Dhaval Sathawara ◽  
Pinky Brahmbhatt
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Approach

A discrete level set approach to image segmentation

2007 ◽  
Vol 1 (4) ◽  
pp. 303-320 ◽  
Author(s):  
Alberto De Santis ◽  
Daniela Iacoviello
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Discrete Level ◽  
Level Set Approach

A novel level set approach for image segmentation with landmark constraints

Optik ◽  
2019 ◽  
Vol 182 ◽  
pp. 257-268 ◽  
Author(s):  
Huizhu Pan ◽  
Wanquan Liu ◽  
Ling Li ◽  
Guanglu Zhou
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Approach
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