scholarly journals Texture Analysis of CT Images for Vascular Segmentation: A Revised Run Length Approach

2005 ◽  
pp. 907-914 ◽  
Author(s):  
Barbara Podda ◽  
Andrea Giachetti
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Run Length ◽  
Vascular Segmentation

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Texture analysis for vascular segmentation from CT images

2005 ◽  
Vol 1281 ◽  
pp. 206-211
Author(s):  
Barbara Podda ◽  
Gianluigi Zanetti ◽  
Andrea Giachetti
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Vascular Segmentation

scholarly journals Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Textural Features ◽  
Mutation Status ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Ct Texture Analysis

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

scholarly journals PD14-07 DIFFERENTIATION BETWEEN CLEAR CELL RENAL CELL CARCINOMAS AND ONCOCYTOMAS USING TEXTURE ANALYSIS OF CT IMAGES

2016 ◽  
Vol 195 (4S) ◽  
Author(s):  
Vinay Duddalwar ◽  
Xuejun Zhang ◽  
Darryl Hwang ◽  
Steven Cen ◽  
Felix Yap ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Renal Cell ◽  
Clear Cell ◽  
Ct Images ◽  
Renal Cell Carcinomas

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Radiology ◽  
2015 ◽  
Vol 276 (3) ◽  
pp. 787-796 ◽  
Author(s):  
Taryn Hodgdon ◽  
Matthew D. F. McInnes ◽  
Nicola Schieda ◽  
Trevor A. Flood ◽  
Leslie Lamb ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Ct Images ◽  
Renal Angiomyolipoma ◽  
Quantitative Ct ◽  
Unenhanced Ct ◽  
Ct Texture Analysis

Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning

2018 ◽  
Vol 29 (5) ◽  
pp. 2207-2217 ◽  
Author(s):  
Urs J. Muehlematter ◽  
Manoj Mannil ◽  
Anton S. Becker ◽  
Kerstin N. Vokinger ◽  
Tim Finkenstaedt ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Texture Analysis ◽  
Vertebral Body ◽  
Ct Images ◽  
Insufficiency Fractures

scholarly journals Diagnosis of Breast Tumors with Sonographic Texture Analysis Using Run-length Matrix

2017 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Ali Abbasian Ardakani ◽  
Afshin Mohammadi ◽  
Akbar Gharbali ◽  
Aram Rostami
Keyword(s):  
Texture Analysis ◽  
Breast Tumors ◽  
Run Length

3D Texture Analysis of Corpus Caliosum Based on MR Images Inpatients with Alzheimer’s Disease and Mild Cognitive Impairment

2014 ◽  
Vol 533 ◽  
pp. 415-420 ◽  
Author(s):  
Wei Fang Liu ◽  
Xu Wang ◽  
Hong Xia
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Texture Analysis ◽  
Three Dimensional ◽  
Texture Features ◽  
Grey Level ◽  
Run Length ◽  
3D Texture ◽  
3D Texture Analysis

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimers disease (AD). Methods: T1-weighted MRI of 18 AD patients, 18 Mild Cognitive Impairment (MCI) patients and 18 normal controls (NC) were selected.3D Texture parameters of the corpus callosum,including contrast, inverse difference moment , entropy, short run emphasis, long run emphasis, grey level nonuniformity, run length nonuniformity and fraction were extracted from the gray level co-occurrence matrix and run length matrix. Finally statistic significance was tested among three groups, and the correlations between parameters and Mini-Mental State Examination (MMSE) scores were calculated. Results: The results showed that the 3D texture features had significant differences (p<0.05) among three groups except grey level nonuniformity and run length nonuniformity that the difference was not significant (p>0.05) between MCI and NC or AD and MCI , and they were correlated with MMSE scores.Conclusions: 3D texture analysis can reflect the pathological changes of corpus callosum in patients with AD and MCI, and it may be helpful to AD early diagnosis.

Sign in / Sign up

Export Citation Format

Share Document