Biomedical Image Segmentation Using Spatial Kernel Fuzzy C-Means Based Level Set Formulation

2012 ◽  
Vol 2 (2) ◽  
pp. 200-205
Author(s):  
Raghotham Reddy Ganta ◽  
Syed Zaheeruddin ◽  
Narsimha Baddiri ◽  
R. Rameshwar Rao
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Biomedical Image ◽  
Level Set Formulation

Integrating Feature Direction Information with a Level Set Formulation for Image Segmentation

2016 ◽  
Vol 6 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Meng Li ◽  
Yi Zhan
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Directional Derivative ◽  
Image Features ◽  
Second Order ◽  
Directional Derivatives ◽  
Level Set Function ◽  
Variational Framework ◽  
Level Set Evolution ◽  
Level Set Formulation

AbstractA feature-dependent variational level set formulation is proposed for image segmentation. Two second order directional derivatives act as the external constraint in the level set evolution, with the directional derivative across the image features direction playing a key role in contour extraction and another only slightly contributes. To overcome the local gradient limit, we integrate the information from the maximal (in magnitude) second-order directional derivative into a common variational framework. It naturally encourages the level set function to deform (up or down) in opposite directions on either side of the image edges, and thus automatically generates object contours. An additional benefit of this proposed model is that it does not require manual initial contours, and our method can capture weak objects in noisy or intensity-inhomogeneous images. Experiments on infrared and medical images demonstrate its advantages.

Image Segmentation with Depth Information via Simplified Variational Level Set Formulation

2017 ◽  
Vol 60 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Lu Tan ◽  
Zhenkuan Pan ◽  
Wanquan Liu ◽  
Jinming Duan ◽  
Weibo Wei ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Depth Information ◽  
Variational Level Set ◽  
Level Set Formulation

scholarly journals Active Contour Driven by Local Region Statistics and Maximum A Posteriori Probability for Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xiaoliang Jiang ◽  
Bailin Li ◽  
Qiang Wang ◽  
Jiajia Liu
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour ◽  
Medical Images ◽  
Bias Field ◽  
Medical Image Segmentation ◽  
Local Region ◽  
Posteriori Probability ◽  
Field Estimation ◽  
Level Set Formulation

This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD) with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances. Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are simultaneously achieved by minimizing the level set formulation. Experimental results demonstrate desirable performance of the proposed method for different medical images with weak boundaries and noise.

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