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.

X-Ray CT Image Segmentation of Asphalt Concrete Based on Fuzzy C-Means

2012 ◽  
Vol 170-173 ◽  
pp. 3444-3448 ◽  
Author(s):  
Jian Jun Wei ◽  
Hai Bin Li ◽  
Cheng Wan
Keyword(s):  
Image Segmentation ◽  
Asphalt Concrete ◽  
Low Density ◽  
Ct Image ◽  
Threshold Values ◽  
Double Peak ◽  
X Ray ◽  
Fuzzy C Means ◽  
Double Peaks ◽  
Image Technique

This study focuses on the threshold segmentation algorithm to obtain the real microstructure of asphalt concrete based on digital image technique, the perlite powder which was a kind of low-density material was put in the asphalt concrete to enhance the density contrast, three different specimens in which added different contents of perlite powder were compacted, and then the asphalt concrete specimens were scanned using x-ray CT to capture the gray images that reflect the density differences of the three constituents such as aggregates, mastic and voids, the CT images were converted to be the histograms. Furthermore, the FCM (Fuzzy C-Means) was demonstrated that it could be utilized to choose proper threshold values and segment images exactly, according to the double peak conditions of the three different histograms, the double peak condition for AC-13 is the best among the three types, a similar double peak features between AK13 and SMA-13 were observed. The results shows that the different contents of perlite powder added in the asphalt concrete can form different double peaks. This is another new method to segment the three constituents of the asphalt concrete exactly.

scholarly journals ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Great Success ◽  
Morphological Operations ◽  
Ct Image ◽  
Feature Maps ◽  
Learning Framework ◽  
Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

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.

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 A Review of Morphological and Chemical Properties of Porous Asphalt

CONSTRUCTION ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 45-49
Author(s):  
N.E. Jasni ◽  
Khairil Azman Masri ◽  
R.P. Jaya
Keyword(s):  
Electron Microscopy ◽  
Chemical Composition ◽  
Asphalt Mixture ◽  
Chemical Properties ◽  
Fine Aggregate ◽  
Air Voids ◽  
Porous Asphalt ◽  
X Ray ◽  
Scanning Electron ◽  
And Chemical Properties

Porous asphalt mixture is also known as gap graded mixture with less amount of fine aggregate has led the mixture contains high air voids, tends to make the mixture less durable and high porousity. Hence, past researchers has investigate on how to increase the strength of porous asphalt mixture by the addition of additive such as fiber and  nanomaterials. The chemical and physical properties of porous asphalt mixture was highlighted in this paper to compare its structure, the bonding between the materials and its chemical composition that exist. This paper reviews on how additive affect the asphalt mixture in terms of Scanning Electron Microscopy (SEM), X-Ray Diffractions (XRD) and Fourier Transform Infrared Spectroscopy (FTIR). These tests are selected to improve the asphalt mixture according to the morphological and chemical properties of porous asphalt. This study is expected to identify the morphological and chemical composition of the materials in asphalt mixture.

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.

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