3D Segmentation of Funnel Flow Boundary During Silo Emptying

2014 ◽  
Vol 19 (2-3) ◽  
pp. 141-149 ◽  
Author(s):  
Selam Waktola ◽  
Laurent Babout ◽  
Krzysztof Grudzien
Keyword(s):  
Image Segmentation ◽  
Interrogation Window ◽  
Stagnant Zone ◽  
In Situ Experiment ◽  
Flow Area ◽  
X Ray ◽  
3D Tomography ◽  
Flow Investigation ◽  
Ct System ◽  
Funnel Flow

Abstract The paper presents an automatic method for segmenting 3D tomography images of a funnel flow area, during silo emptying process. For generating 3D images the silo model was scanned using X-ray Computed Tomography (CT) system. The method has been applied for a chosen single slice from 3D image. The image segmentation is based on the variance of pixels calculation in defined interrogation window (or kernel). The analysis of Signalto- Noise-Ratio (SNR) of the given image allows to improve the contrast in the image and facilitate the detection the boundary between funnel area and stagnant zone. The obtained results of image segmentation show a high potential in the silo flow investigation using in-situ experiment using X-ray visualization. Additionally, the study indicates that, the separation of the silo area into the funnel and stagnant zone parts is a very challenging task especially for the top and bottom area of silo where the contrast is the smallest.

scholarly journals 3D Reconstruction of Funnel Flow Boundary Using Automatic Point Set Extraction

2015 ◽  
Vol 20 (3) ◽  
pp. 35-43
Author(s):  
Selam Waktola ◽  
Krzysztof Grudzien ◽  
Laurent Babout
Keyword(s):  
Boundary Line ◽  
Filter Function ◽  
Local Flow ◽  
3D Images ◽  
X Ray ◽  
3D Tomography ◽  
Segmented Image ◽  
Flow Boundary ◽  
Point Set ◽  
Funnel Flow

Abstract The paper presents an automatic point set extraction method for reconstructing 3D tomography images of funnel flow boundary. The method clearly shows the boundary between the funnel flow and stagnant zone during silo discharging process. After adjusting the contrast of the original X-ray CT image and applying filter function, the intensity profile of the image shows a high jump corresponding to the local flow boundary position at a specific height of the silo model. By extracting and connecting those jump points gave us a boundary line of the funnel flow from the stagnant. The outcome of segmented image opens a door for analysing further about funnel flow in 3D images.

scholarly journals Image Segmentation Techniques in Bone Structure Psychiatry

2020 ◽  
Vol 9 (07) ◽  
pp. 25102-25112
Author(s):  
Ajayi Olayinka Adedoyin ◽  
Olamide Timothy Tawose ◽  
Olu Sunday Adetolaju
Keyword(s):  
Image Segmentation ◽  
Bone Structure ◽  
Performance Comparison ◽  
Imaging Systems ◽  
Intrinsic Properties ◽  
X Ray ◽  
Practical Applications ◽  
X Ray Imaging ◽  
Segmentation Algorithms ◽  
Bone Structures

Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images

2015 ◽  
Vol 117 (18) ◽  
pp. 183102 ◽  
Author(s):  
Arjun S. Kumar ◽  
Pratiti Mandal ◽  
Yongjie Zhang ◽  
Shawn Litster
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Phase Contrast ◽  
X Ray ◽  
Computed Tomography Images ◽  
X Ray Computed

Electron Density and Effective Atomic Number Measurement by Using a Newly Developing Photon Counting CT System

2020 ◽  
Vol 38 ◽  
pp. 93-99
Author(s):  
Hiroshi Sakurai ◽  
Kazushi Hoshi ◽  
Yosuke Harasawa ◽  
Daiki Ono ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Electron Density ◽  
Atomic Number ◽  
Photon Counting ◽  
Effective Atomic Number ◽  
Image Data ◽  
Detector Response ◽  
Simple Method ◽  
Attenuation Coefficients ◽  
X Ray ◽  
Ct System

We developed the photon counting CT system by using a conventional laboratory X-ray source and a CdTe line sensor. Attenuation coefficients were obtained from the measured CT image data. Our suggested method for deriving the electron density and effective atomic number from the measured attenuation coefficients was tested experimentally. The accuracy of the electron densities and effective atomic numbers are about <5 % (the averages of absolute values are 2.6 % and 3.1 %, respectively) for material of 6< Z and Zeff <13. Our suggested simple method, in which we do not need the exact source X-ray spectrum and detector response function, achieves comparable accuracy to the previous reports.

A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system

2020 ◽  
Vol 35 (3) ◽  
pp. 252-265 ◽  
Author(s):  
V. Nguyen ◽  
J. De Beenhouwer ◽  
J. G. Sanctorum ◽  
S. Van Wassenbergh ◽  
S. Bazrafkan ◽  
...  
Keyword(s):  
Low Cost ◽  
Calibration Procedure ◽  
Cone Beam ◽  
X Ray ◽  
Ct System ◽  
Geometry Calibration
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