scholarly journals Resin Recovery and the use of Computed Tomography for Quantitative Image Analysis of Railway Ballast

2014 ◽  
Author(s):  
L. Le Pen ◽  
S. Ahmed ◽  
A. Zervos J. Harkness ◽  
W. Powrie
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Quantitative Image Analysis ◽  
Railway Ballast ◽  
Quantitative Image

Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade

2021 ◽  
pp. 679-694
Author(s):  
Alessandra Pulvirenti ◽  
Rikiya Yamashita ◽  
Jayasree Chakraborty ◽  
Natally Horvat ◽  
Kenneth Seier ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Ct Scan ◽  
Predictive Value ◽  
Quantitative Computed Tomography ◽  
Tumor Grade ◽  
Quantitative Image Analysis ◽  
Arterial Phase ◽  
Imaging Method ◽  
Quantitative Image

PURPOSE The therapeutic management of pancreatic neuroendocrine tumors (PanNETs) is based on pathological tumor grade assessment. A noninvasive imaging method to grade tumors would facilitate treatment selection. This study evaluated the ability of quantitative image analysis derived from computed tomography (CT) images to predict PanNET grade. METHODS Institutional database was queried for resected PanNET (2000-2017) with a preoperative arterial phase CT scan. Radiomic features were extracted from the primary tumor on the CT scan using quantitative image analysis, and qualitative radiographic descriptors were assessed by two radiologists. Significant features were identified by univariable analysis and used to build multivariable models to predict PanNET grade. RESULTS Overall, 150 patients were included. The performance of models based on qualitative radiographic descriptors varied between the two radiologists (reader 1: sensitivity, 33%; specificity, 66%; negative predictive value [NPV], 63%; and positive predictive value [PPV], 37%; reader 2: sensitivity, 45%; specificity, 70%; NPV, 72%; and PPV, 47%). The model based on radiomics had a better performance predicting the tumor grade with a sensitivity of 54%, a specificity of 80%, an NPV of 81%, and a PPV of 54%. The inclusion of radiomics in the radiographic descriptor models improved both the radiologists' performance. CONCLUSION CT quantitative image analysis of PanNETs helps predict tumor grade from routinely acquired scans and should be investigated in future prospective studies.

scholarly journals Quantitative image analysis for the detection of motion artefacts in coronary artery computed tomography

2009 ◽  
Vol 26 (1) ◽  
pp. 77-87 ◽  
Author(s):  
Wisnumurti Kristanto ◽  
Peter M. van Ooijen ◽  
Riksta Dikkers ◽  
Marcel J. Greuter ◽  
Felix Zijlstra ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Coronary Artery ◽  
Quantitative Image Analysis ◽  
Motion Artefacts ◽  
Quantitative Image

scholarly journals Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis

2018 ◽  
Vol 69 ◽  
pp. 134-139 ◽  
Author(s):  
Rachel B. Ger ◽  
Daniel F. Craft ◽  
Dennis S. Mackin ◽  
Shouhao Zhou ◽  
Rick R. Layman ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Head And Neck ◽  
Quantitative Image Analysis ◽  
Quantitative Image ◽  
Practical Guidelines

Digital image acquisition, processing, and analysis in a polymer microscopy laboratory

1994 ◽  
Vol 52 ◽  
pp. 454-455
Author(s):  
Vinod K. Berry ◽  
Xiao Zhang
Keyword(s):  
United States ◽  
Image Analysis ◽  
Digital Image ◽  
Image Acquisition ◽  
Image Transmission ◽  
The United States ◽  
Quantitative Image Analysis ◽  
Quantitative Image

In recent years it became apparent that we needed to improve productivity and efficiency in the Microscopy Laboratories in GE Plastics. It was realized that digital image acquisition, archiving, processing, analysis, and transmission over a network would be the best way to achieve this goal. Also, the capabilities of quantitative image analysis, image transmission etc. available with this approach would help us to increase our efficiency. Although the advantages of digital image acquisition, processing, archiving, etc. have been described and are being practiced in many SEM, laboratories, they have not been generally applied in microscopy laboratories (TEM, Optical, SEM and others) and impact on increased productivity has not been yet exploited as well.In order to attain our objective we have acquired a SEMICAPS imaging workstation for each of the GE Plastic sites in the United States. We have integrated the workstation with the microscopes and their peripherals as shown in Figure 1.

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