scholarly journals Multi-Scale Adaptive Level Set Segmentation Method Based on Saliency

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 153031-153040
Author(s):  
Zhang Dan ◽  
Chen C. L. Philip ◽  
Yang He ◽  
Li Tieshan
Keyword(s):  
Level Set ◽  
Segmentation Method ◽  
Multi Scale ◽  
Level Set Segmentation

Fast Level Set Segmentation Method in Medical Multi-sensor Images Detection

2012 ◽  
Vol 4 (23) ◽  
pp. 475-482 ◽  
Author(s):  
Huimin Lu ◽  
Yujie Li ◽  
Lifeng Zhang ◽  
Shiyuan Yang ◽  
Seiichi Serikawa
Keyword(s):  
Level Set ◽  
Segmentation Method ◽  
Level Set Segmentation

Level Set Segmentation Method in Medical Image Segmentation Research and Application

2012 ◽  
Vol 157-158 ◽  
pp. 1012-1015 ◽  
Author(s):  
Yu Miao ◽  
Wei Li Shi
Keyword(s):  
Image Segmentation ◽  
White Matter ◽  
Level Set ◽  
Medical Image ◽  
Region Of Interest ◽  
Medical Image Segmentation ◽  
The Other ◽  
Segmentation Method ◽  
Level Set Segmentation ◽  
Mri Image

Medical image segmentation can be divided into two categories: one is the region of interest (ROI) identification; the other is the description of the integrity and the extraction of interest region. The emergence of the level set method greatly promoted the development of medical image segmentation. This paper studies three different level set segmentation algorithm to achieve the effective segmentation for brain gray matter and white matter of MRI image.

scholarly journals Multi-Projection Segmentation on Dental Cone Beam Computed Tomography Images Using Level Set Method

2021 ◽  
Vol 5 (2) ◽  
pp. 130-139
Author(s):  
Fahmi Syuhada ◽  
Rarasmaya Indraswari ◽  
Agus Zainal Arifin ◽  
Dini Adni Navastara
Keyword(s):  
Computed Tomography ◽  
Level Set ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Projection Data ◽  
Segmentation Method ◽  
Computed Tomography Images ◽  
Level Set Segmentation ◽  
Level Method ◽  
Sensitivity Specificity

Segmentation of dental Cone-beam computed tomography (CBCT) images based on Boundary Tracking has been widely used in recent decades. Generally, the process only uses axial projection data of CBCT where the slices image that representing the tip of the tooth object have decreased in contrast which impact to difficult to distinguish with background or other elements. In this paper we propose the multi-projection segmentation method by combining the level set segmentation result on three projections to detect the tooth object more optimally. Multiprojection is performed by decomposing CBCT data which produces three projections called axial, sagittal and coronal projections. Then, the segmentation based on the set level method is implemented on the slices image in the three projections. The results of the three projections are combined to get the final result of this method. This proposed method obtains evaluation results of accuracy, sensitivity, specificity with values of 97.18%, 88.62%, and 97.61%, respectively.

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