scholarly journals A-271 Predictive capabilities of CT-derived 3D Texture Features for Masaoka and WHO classifications of Thymic Epithelial Neoplasms

2021 ◽  
Author(s):  
Christian Blüthgen
Keyword(s):  
Texture Features ◽  
3D Texture ◽  
Epithelial Neoplasms

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.

3D Texture Features Mining for MRI Brain Tumor Identification

3D Research ◽  
2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Mohd Shafry Mohd Rahim ◽  
Tanzila Saba ◽  
Fatima Nayer ◽  
Afraz Zahra Syed
Keyword(s):  
Brain Tumor ◽  
Texture Features ◽  
Mri Brain ◽  
3D Texture ◽  
Tumor Identification ◽  
Features Mining

scholarly journals Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Daniel Markel ◽  
Curtis Caldwell ◽  
Hamideh Alasti ◽  
Hany Soliman ◽  
Yee Ung ◽  
...  
Keyword(s):  
Lung Carcinoma ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Ground Truth ◽  
Standard Uptake Value ◽  
Structural Features ◽  
3D Texture ◽  
Positron Emission ◽  
Fuzzy Clustering Method ◽  
And Performance

Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians.

scholarly journals Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Ahmad Chaddad ◽  
Camel Tanougast
Keyword(s):  
Colorectal Cancer ◽  
Performance Metrics ◽  
Principal Component ◽  
Texture Features ◽  
Intraepithelial Neoplasia ◽  
Cell Types ◽  
Abnormal Cell ◽  
Contour Segmentation ◽  
3D Texture ◽  
The Continuum

Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with a p value cutoff of p<0.01. Features selected were reduced with a principal component analysis (PCA), which accounted for 97% of the cumulative variance from significant features. The simulation results identified 158 significant features based on ANOVA from a total of 624 texture features extracted from GLCMs. Performance metrics of ABC discrimination based on significant texture features showed 92.59% classification accuracy, 100% sensitivity, and 94.44% specificity. These findings suggest that texture features extracted from GLCMs are sensitive enough to discriminate between the ABC types and offer the opportunity to predict cell characteristics of colorectal cancer.

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