A fuzzy segmentation method for Computed Tomography images

2007 ◽  
Vol 1 (1) ◽  
pp. 79
Author(s):  
Martin Tabakov
Keyword(s):  
Computed Tomography ◽  
Segmentation Method ◽  
Computed Tomography Images ◽  
Fuzzy Segmentation

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.

Quantitative evaluation of emission-computed tomography images based on fuzzy segmentation

1996 ◽  
Author(s):  
Thomas Schmitt ◽  
Heinz-Dieter Gebauer ◽  
Richard Freyer ◽  
Liane Oehme ◽  
Michael Andreeff ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Quantitative Evaluation ◽  
Emission Computed Tomography ◽  
Computed Tomography Images ◽  
Emission Computed ◽  
Fuzzy Segmentation

Creeping Contours: A Multilabel Image Segmentation Method for Extracting Boundary Surfaces of Parts in Volumetric Images

2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Haitham Shammaa ◽  
Hiromasa Suzuki ◽  
Yutaka Ohtake
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Initial Contour ◽  
Segmentation Method ◽  
Mechanical Parts ◽  
Computed Tomography Images ◽  
Volumetric Images ◽  
Speed Function ◽  
Boundary Surfaces ◽  
Grayscale Images

In this work, we introduce a method named creeping contours for image segmentation into component parts for the purpose of extracting the boundary surfaces of these parts. Creeping contours are contours that expand following a speed function defined by the gradient and curvature at contour points, starting from an initial contour position defined either manually or automatically. Contours in the image creep simultaneously at different speeds, while labels are assigned to contour pixels by the defined creeping condition. We also demonstrate the effectiveness of the proposed method by segmenting 2D grayscale images and 3D volumetric computed tomography images of mechanical parts into multiple segments and generating the boundary surfaces of these parts.

scholarly journals Interactive Segmentation of Pancreases in Abdominal Computed Tomography Images and Its Evaluation Based on Segmentation Accuracy and Interaction Costs

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Hotaka Takizawa ◽  
Takenobu Suzuki ◽  
Hiroyuki Kudo ◽  
Toshiyuki Okada
Keyword(s):  
Computed Tomography ◽  
Ct Images ◽  
Graph Cut ◽  
Medical Doctor ◽  
Seed Selection ◽  
Segmentation Method ◽  
Interactive Segmentation ◽  
Abdominal Computed Tomography ◽  
Computed Tomography Images ◽  
Interaction Costs

The present paper proposed an interactive segmentation method of pancreases in abdominal computed tomography (CT) images based on the anatomical knowledge of medical doctors and the statistical information of pancreas shapes. This segmentation method consisted of two phases: training and testing. In the training phase, pancreas regions were manually extracted from sample CT images for training, and then a probabilistic atlas (PA) was constructed from the extracted regions. In the testing phase, a medical doctor selected seed voxels for a pancreas and background in a CT image for testing by use of our graphical user interface system. The homography transformation was used to fit the PA to the seeds. The graph cut technique whose data term was weighted by the transformed PA was applied to the test image. The seed selection, the atlas transformation, and the graph cut were executed iteratively. This doctor-in-the-loop segmentation method was applied to actual abdominal CT images of fifteen cases. The experimental results demonstrated that the proposed method was more accurate and effective than the conventional graph cut.

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