A Robust Line Filter for Automatic X-Ray/CT Image Segmentation

2014 ◽  
Vol 721 ◽  
pp. 783-787
Author(s):  
Shao Hu Peng ◽  
Hyun Do Nam ◽  
Yan Fen Gan ◽  
Xiao Hu
Keyword(s):  
Multiscale Analysis ◽  
Automatic Segmentation ◽  
Local Variation ◽  
Recognition System ◽  
Ct Images ◽  
Gray Level ◽  
Ct Image ◽  
Image Pyramid ◽  
Level Variation ◽  
X Ray

Automatic segmentation of the line-like regions plays a very important role in the automatic recognition system, such as automatic cracks recognition in X-ray images, automatic vessels segmentation in CT images. In order to automatically segment line-like regions in the X-ray/CT images, this paper presents a robust line filter based on the local gray level variation and multiscale analysis. The proposed line filter makes usage of the local gray level and its local variation to enhance line-like regions in the X-ray/CT image, which can well overcome the problems of the image noises and non-uniform intensity of the images. For detecting various sizes of line-like regions, an image pyramid is constructed based on different neighboring distances, which enables the proposed filter to analyze different sizes of regions independently. Experimental results showed that the proposed line filter can well segment various sizes of line-like regions in the X-ray/CT images, which are with image noises and non-uniform intensity problems.

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

X-Ray Microfocus Radioscopy and Computed Tomography for Failure Analysis

1996 ◽  
Author(s):  
R.H. Bossi ◽  
D.A. Cross ◽  
R.A. Mickelsen
Keyword(s):  
Computed Tomography ◽  
Failure Analysis ◽  
Conventional Radiography ◽  
Ct Images ◽  
Ct Image ◽  
Video Monitor ◽  
Cross Sectional ◽  
X Ray ◽  
Technical Accuracy ◽  
Conventional Film

Abstract X-ray microfocus radioscopy and computed tomography (CT) offer detailed information on the internal assembly and material condition of objects under failure analysis investigation. Using advanced systems for the acquisition of radioscopic and CT images, failure analysis investigations are improved in technical accuracy at a reduced schedule and cost over alternative approaches. A versatile microfocus radioscopic system with CT capability has been successfully implemented as a standard tool in the Boeing Defense & Space Group Failure Analysis Laboratory. Using this tool, studies of electronic, electromechanical and composite material items have been performed. Such a system can pay for itself within two years through higher productivity of the laboratory, increased laboratory value to the company and resolution of critical problems whose worth far exceeds the value of the equipment. The microfocus X-ray source provides projection magnification images that exceed the sensitivity to fine detail that can be obtained with conventional film radiography. Radioscopy, which provides real-time images on a video monitor, allows objects to be readily manipulated and oriented for optimum x-ray evaluation, or monitored during dynamic processes to check performance. Combined with an accurate manipulating stage and data acquisition system x-ray measurements can be used for CT image reconstruction. The CT image provides a cross sectional view of the interior of an object without the interference of superposition of features found in conventional radiography. Accurate dimensional measurements and material constituent identification are possible from the CT images. By taking multiple, contiguous CT slices entire three dimensional data files can be generated of objects.

Experimental Evaluation of X-Ray CT Images of Asphalt Mixture Based on Threshold Segmentation Principle

2012 ◽  
Vol 482-484 ◽  
pp. 327-330 ◽  
Author(s):  
Jian Jun Wei ◽  
Hai Bin Li ◽  
Cheng Wan
Keyword(s):  
Image Segmentation ◽  
Experimental Evaluation ◽  
Asphalt Mixture ◽  
Ct Images ◽  
Ct Image ◽  
Double Peak ◽  
New Approach ◽  
Air Voids ◽  
X Ray ◽  
Different Levels

In order to select threshold of CT image of asphalt mixture for image segmentation more accurately, the perlite powder was added in asphalt mixture to increase the density contrast, three different mixture gradations in which added different levels of perlite powder were prepared and compacted in laboratory, the X-ray CT was used to scan the asphalt mixture specimen to obtain continuous CT images, the CT images were transformed to be histograms which formed double peak. Through comparing with the double peak situation of three mixture types, AC-13 has the best double peak situation, AK-13 and SMA-13 have similar feature of histogram. The results indicate that the addition levels of perlite powder influence the double peak situation significantly. This new approach is an effective way to identify aggregates, mastic and air voids exactly.

scholarly journals Fully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
ZhenZhou Wang ◽  
Cunshan Zhang ◽  
Ticao Jiao ◽  
MingLiang Gao ◽  
Guofeng Zou
Keyword(s):  
Automatic Segmentation ◽  
Three Dimensional ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Ct Image ◽  
Three Dimensional Reconstruction ◽  
Robust Segmentation ◽  
Multiple Thresholds ◽  
Dimensional Reconstruction ◽  
Slope Difference

Automatic segmentation and three-dimensional reconstruction of the liver is important for liver disease diagnosis and surgical treatment. However, the shape of the imaged 2D liver in each CT image changes dramatically across the slices. In all slices, the imaged 2D liver is connected with other organs, and the connected organs also vary across the slices. In many slices, the intensities of the connected organs are the same with that of the liver. All these facts make automatic segmentation of the liver in the CT image an extremely difficult task. In this paper, we propose a heuristic approach to segment the liver automatically based on multiple thresholds. The thresholds are computed based on the slope difference distribution that has been proposed and verified in the previous research. Different organs in the CT image are segmented with the automatically computed thresholds, respectively. Then, different segmentation results are combined to delineate the boundary of the liver robustly. After the boundaries of the 2D liver in all the slices are identified, they are combined to form the 3D shape of the liver with a global energy minimization function. Experimental results verified the effectiveness of all the proposed image processing algorithms in automatic and robust segmentation of the liver in CT images.

scholarly journals Selection in a 3D microtomographic image the region with the highest quality

2019 ◽  
pp. 160-168 ◽  
Author(s):  
A S Kornilov ◽  
I V Safonov ◽  
A V Goncharova ◽  
I V Yakimchuk
Keyword(s):  
Flow Simulation ◽  
Visual Quality ◽  
Quality Measures ◽  
Ct Images ◽  
Ct Image ◽  
Micro Ct ◽  
X Ray ◽  
Image Slice ◽  
Selection Of

We present an algorithm for processing of X-ray microtomographic (micro-CT) images that allows automatic selection of a sub-volume having the best visual quality for further mathematical simulation, for example, flow simulation. Frequently, an investigated sample occupies only a part of a volumetric image or the sample can be into a holder; a part of the image can be cropped. For each 2D slice across the Z-axis of an image, the proposed method locates a region corresponding to the sample. We explored applications of several existing blind quality measures for an estimation of the visual quality of a micro-CT image slice. Some of these metrics can be applied to ranking the image regions according to their quality. Our method searches for a cubic area located inside regions belonging to the sample and providing the maximal sum of the quality measures of slices crossing the cube across the Z-axis. The proposed technique was tested on synthetic and real micro-CT images of rocks.

scholarly journals Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

2019 ◽  
Vol 9 (24) ◽  
pp. 5507 ◽  
Author(s):  
Fernando Cervantes-Sanchez ◽  
Ivan Cruz-Aceves ◽  
Arturo Hernandez-Aguirre ◽  
Martha Alicia Hernandez-Gonzalez ◽  
Sergio Eduardo Solorio-Meza
Keyword(s):  
Neural Networks ◽  
Coronary Arteries ◽  
Multiscale Analysis ◽  
State Of The Art ◽  
Binary Classification ◽  
Automatic Segmentation ◽  
Classification Rate ◽  
Test Set ◽  
X Ray ◽  
Vessel Extraction

This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging.

scholarly journals Research on 3D crack segmentation of CT images of oil rock core

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258463
Author(s):  
Yongning Zou ◽  
Gongjie Yao ◽  
Jue Wang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Ct Images ◽  
Gray Level ◽  
Ct Image ◽  
Median Filtering ◽  
Background Region ◽  
Level Set Segmentation ◽  
Binary Segmentation ◽  
Average Gray Level

In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.

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