Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images

2016 ◽  
Vol 45 (5) ◽  
pp. 1476-1484 ◽  
Author(s):  
Wu Zhou ◽  
Lijuan Zhang ◽  
Kaixin Wang ◽  
Shuting Chen ◽  
Guangyi Wang ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Mr Images ◽  
Hepatocellular Carcinomas ◽  
Contrast Enhanced

scholarly journals The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoguang Li ◽  
Hong Guo ◽  
Chao Cong ◽  
Huan Liu ◽  
Chunlai Zhang ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Roc Curve ◽  
Breast Imaging ◽  
Statistical Significance ◽  
Texture Features ◽  
Imaging Findings ◽  
Mr Images ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Occurrence Matrix

PurposeTo explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs).MethodsA total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches.ResultsRegarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively.ConclusionTA based on DCE-MR images has potential in differentiating BPTs and BMPTs.

scholarly journals CHARACTERIZATION OF HIPPOCAMPUS ON EPILEPTIC PATIENTS ON MRI USING TEXTURE ANALYSIS TECHNIQUES

2021 ◽  
Vol 9 (1) ◽  
pp. 164-168
Author(s):  
Tasneem Abdulrazig Mohamed Sayed ◽  
Fatima Yousif Mohammed ◽  
Maha Esmeal Ahmed
Keyword(s):  
Texture Analysis ◽  
Brain Mri ◽  
Score Function ◽  
Image Texture ◽  
Mr Images ◽  
Image Texture Analysis ◽  
Analysis Techniques ◽  
Classification Score ◽  
Epileptic Patients

The aim of this study was to characterize the hippocampus in Sudanese epileptic patients in MR images using image texture analysis techniques in order to differentiate hippocampus between the normal and epileptic patient. There were two groups of the patients were examined by using Signal-GE 1.5Tesla MR Scanner which was used with patients with known epilepsy and normal T1 weighted brain. MRI finding patients, 101 and 105 patients respectively examined in period from December 2017- March 2018, where the variables of the study were MRI images entered to the IDL program as input for further analysis, using window 3*3 the images texture was extracted from hippocampus (head, body and tail) that include, mean, STD, variance, energy, and entropy then the comparison was made to differentiate between the normal and abnormal hippocampus. The extracted feature classified using linear discriminate analysis. The classification score function is used to classify the hippocampus classes was as flows: Epileptic= (.271×mean) + (.026×variance) + (7.475× Part) -32.134 Normal= (.240×mean) + (.052×variance) + (2.960× Part) -13.684 The study confirmed that it’s possible to differentiate between normal and epileptic hippocampus body, head, and tail in sagittal section texturally. The result showed that the classification result is best in the tail where higher classification accuracy will be achieved followed by body and then head.

CONTRAST ENHANCED SONOGRAPHY IN CHARACTERIZATION OF ENLARGED LYMPH NODES OF THE NECK IN CHILDREN

2005 ◽  
Vol 26 (S 1) ◽  
Author(s):  
U Zaleska-Dorobisz ◽  
K Domagala-Pêkalska ◽  
D Sokolowska ◽  
E Czapiga
Keyword(s):  
Lymph Nodes ◽  
Contrast Enhanced

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.

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