scholarly journals Fracture Segmentation Method Based on Contour Evolution and Gradient Direction Consistency in Sequence of Coal Rock CT Images

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Zhiwei Li ◽  
Guoying Zhang
Keyword(s):  
Dimensional Space ◽  
Rock Fracture ◽  
Ct Images ◽  
Fracture Networks ◽  
Segmentation Method ◽  
Gradient Direction ◽  
Consistency Model ◽  
Weak Boundaries ◽  
Coal Rock ◽  
Contour Evolution

The coal rock exhibits obvious heterogeneity and anisotropy after a long-term geological evolution in nature. Therefore, the coal rock CT (computed tomography) image shows uneven grey scale, low contrast, and the fractures with weak boundaries. The accurate segmentation of the coal rock fracture networks is challenging. In this paper, a segmentation method of fractures based on contour evolution and gradient direction consistency is proposed to accurately segment the fracture networks in the sequence of coal rock CT images. According to the contour variation rule of the fractures in the discrete 3D (three dimensional) space formed by the sequence of CT images, the fracture contour evolution model (FCEM) is constructed and the preliminary segmentation results of fractures are obtained from FCEM. A 3D adaptive median filtering (3DAMF) and a 3D bilateral filtering (3DBF) are proposed. The high density miscellaneous point noises in the coal rock CT images are filtered by the 3DAMF. And the boundaries of fractures are enhanced by 3DBF. According to the similarity of the preliminary segmentation results of fractures and the real contours of fractures, the preliminary segmentation results of fractures are optimized based on the gradient direction consistency model (GDCM) proposed in this paper to obtain the accurate boundaries of fractures. The fracture segmentation method proposed in this paper can obtain accurate boundaries of fractures with weak boundaries, and the experimental results show that the segmentation efficiency for sequence is high and adaptability is strong.

scholarly journals Intelligent Algorithms-Based CT Image Segmentation in Patients with Cardiovascular Diseases and Realization of Visualization Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianhua Huang
Keyword(s):  
Cardiovascular Diseases ◽  
Blood Vessel ◽  
Ct Images ◽  
Ct Image ◽  
Segmentation Method ◽  
Intelligent Algorithms ◽  
Curved Planar Reformation ◽  
Cylinder Model ◽  
Visualization Algorithms ◽  
Coronary Artery Tree

The study focused on the intelligent algorithms-based segmentation of computed tomography (CT) images of patients with cardiovascular diseases (CVD) and the realization of visualization algorithms. The first step was to design a method for precise segmentation under the cylinder model based on the coronary body data of the coarse segmentation, and then the principles of different visualization algorithms were discussed. The results showed that the precise segmentation method can effectively eliminate most of the branches and calcified lesions; curved planar reformation (CPR) and straightened CPR can display the entire blood vessel on one image; and spherical CPR can display the complete coronary artery tree on an image, so that a problem with a certain blood vessel can be quickly found. In conclusion, the precise segmentation of CT images of CVD and visualization algorithm based on the cylinder model have clinical significance in the diagnosis of CVD.

scholarly journals From invasion percolation to flow in rock fracture networks

2012 ◽  
Vol 391 (1-2) ◽  
pp. 264-277 ◽  
Author(s):  
Salomon J. Wettstein ◽  
Falk K. Wittel ◽  
Nuno A.M. Araújo ◽  
Bill Lanyon ◽  
Hans J. Herrmann
Keyword(s):  
Rock Fracture ◽  
Invasion Percolation ◽  
Fracture Networks ◽  
Rock Fracture Networks

scholarly journals Multi-scale approach to invasion percolation of rock fracture networks

2014 ◽  
Vol 519 ◽  
pp. 353-363 ◽  
Author(s):  
Ali N. Ebrahimi ◽  
Falk K. Wittel ◽  
Nuno A.M. Araújo ◽  
Hans J. Herrmann
Keyword(s):  
Rock Fracture ◽  
Invasion Percolation ◽  
Fracture Networks ◽  
Multi Scale ◽  
Rock Fracture Networks

scholarly journals Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Zhenghao Shi ◽  
Jiejue Ma ◽  
Minghua Zhao ◽  
Yonghong Liu ◽  
Yaning Feng ◽  
...  
Keyword(s):  
Region Growing ◽  
Disease Diagnosis ◽  
Correction Method ◽  
Ct Images ◽  
Segmentation Method ◽  
Lung Segmentation ◽  
Multiple Strategies ◽  
Simple Strategy ◽  
Growing Strategy ◽  
Ct Slices

Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

scholarly journals Study on threshold segmentation of multi-resolution 3D human brain CT image

2018 ◽  
Vol 11 (06) ◽  
pp. 1850037
Author(s):  
Ling-ling Cui ◽  
Hui Zhang
Keyword(s):  
Human Brain ◽  
Signal To Noise Ratio ◽  
Ct Images ◽  
Pathological Diagnosis ◽  
Ct Image ◽  
Segmentation Method ◽  
Signal To Noise ◽  
Threshold Segmentation ◽  
Edge Pixel ◽  
Brain Ct

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images, a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper. In this method, first, original 3D human brain image information is collected, and CT image filtering is performed to the collected information through the gradient value decomposition method, and edge contour features of the 3D human brain CT image are extracted. Then, the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points, and the 3D human brain CT image is reconstructed with the salient feature point as center. Simulation results show that the method proposed in this paper can provide accuracy up to 100% when the signal-to-noise ratio is 0, and with the increase of signal-to-noise ratio, the accuracy provided by this method is stable at 100%. Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is significantly better than traditional methods in pathological feature estimation accuracy, and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

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