A Novel Multiphase Segmentation Method for Interpreting the 3d Mesoscopic Structure of Asphalt Mixture Using Ct Images

2021 ◽  
Author(s):  
Changli Li ◽  
Yange Li ◽  
Zheng Han ◽  
Yinfei Du ◽  
Mohammed Ameen Mohammed ◽  
...  
Keyword(s):  
Asphalt Mixture ◽  
Ct Images ◽  
Segmentation Method ◽  
Mesoscopic Structure ◽  
Multiphase Segmentation

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.

Gradation measurement of asphalt mixture by X-Ray CT images and digital image processing methods

Measurement ◽  
2019 ◽  
Vol 132 ◽  
pp. 377-386 ◽  
Author(s):  
Chao Xing ◽  
Huining Xu ◽  
Yiqiu Tan ◽  
Xueyan Liu ◽  
Changhong Zhou ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Asphalt Mixture ◽  
Ct Images ◽  
Processing Methods ◽  
X Ray

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.

scholarly journals Automated and Robust Organ Segmentation for 3D-based Internal dose Calculation

2021 ◽  
Author(s):  
Mahmood Nazari ◽  
Luis David Jimenez-Franco ◽  
Michael Schroeder ◽  
Andreas Kluge ◽  
Marcus Bronzel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dose Calculation ◽  
Ct Images ◽  
Daily Routine ◽  
Segmentation Method ◽  
Spread Function ◽  
Adequate Accuracy ◽  
Segmen Tation ◽  
Medical Physicists ◽  
Kidney Segmentation

Abstract Purpose: In this work we address image segmentation within dosimetry using deep learning and make three main contributions: a) to extend and op- timize the architecture of an existing Convolutional Neural Network (CNN) in order to obtain a fast, robust and accurate Computed Tomography (CT) based organ segmentation method for kidneys and livers; b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; c) to evaluate dosimetry results obtained using automated organ segmentation in comparison to manual segmentation done by two independent experts. Methods: We adapted a performant deep learning approach using CT-images to calculate organ boundaries with sufficiently high and adequate accuracy and processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the ac- tivity values from quantitatively reconstructed SPECT images for ”volumet- ric”/3D dosimetry. The retrieved activities were used to perform dosimetry calculations considering the kidneys as source organ. Results: The computational expenses of the algorithm was adequate enough to be used in clinical daily routine, required minimum pre-processing and per- formed within an acceptable accuracy of 93 . 4% for liver segmentation and of 94 . 1% for kidney segmentation. Additionally, kidney self-absorbed doses calcu- lated using automated segmentation differed 6 . 3% from dosimetries performed by two medical physicists in 8 patients. Conclusion: The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radio-pharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmen- tation methodology based on CT images accelerates the organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images.Trial registration: EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13

scholarly journals Automatic clustering method to segment COVID-19 CT images

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244416
Author(s):  
Mohamed Abd Elaziz ◽  
Mohammed A. A. Al-qaness ◽  
Esraa Osama Abo Zaid ◽  
Songfeng Lu ◽  
Rehab Ali Ibrahim ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Threshold Value ◽  
Ct Images ◽  
Optimal Number ◽  
Clustering Methods ◽  
Segmentation Method ◽  
Density Peaks ◽  
Automatic Clustering ◽  
Proposed Model ◽  
Density Peaks Clustering

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

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