scholarly journals Characterization of PET/CT images using texture analysis: the past, the present… any future?

2016 ◽  
Vol 44 (1) ◽  
pp. 151-165 ◽  
Author(s):  
Mathieu Hatt ◽  
Florent Tixier ◽  
Larry Pierce ◽  
Paul E. Kinahan ◽  
Catherine Cheze Le Rest ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
The Past ◽  
Pet Ct

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.

scholarly journals Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study

2019 ◽  
Vol 92 (1101) ◽  
pp. 20190286 ◽  
Author(s):  
Emine Acar ◽  
Asım Leblebici ◽  
Berat Ender Ellidokuz ◽  
Yasemin Başbınar ◽  
Gamze Çapa Kaya
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Analysis ◽  
Area Under The Curve ◽  
Ct Images ◽  
Metastatic Lesions ◽  
Psma Pet ◽  
Pet Ct ◽  
Sclerotic Lesions ◽  
Psma Expression

Objective:Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated.Methods:We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning.Results:In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions.Conclusions:Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer.Advances in knowledge:Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.

Characterization of Portal Vein Thrombosis (Neoplastic Versus Bland) on CT Images Using Software-Based Texture Analysis and Thrombus Density (Hounsfield Units)

2016 ◽  
Vol 207 (5) ◽  
pp. W81-W87 ◽  
Author(s):  
Rodrigo Canellas ◽  
Farhad Mehrkhani ◽  
Manuel Patino ◽  
Avinash Kambadakone ◽  
Dushyant Sahani
Keyword(s):  
Portal Vein ◽  
Texture Analysis ◽  
Portal Vein Thrombosis ◽  
Ct Images ◽  
Hounsfield Units ◽  
Vein Thrombosis

scholarly journals Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa

2012 ◽  
Vol 41 (6) ◽  
pp. 475-480 ◽  
Author(s):  
JV Raja ◽  
M Khan ◽  
VK Ramachandra ◽  
O Al-Kadi
Keyword(s):  
Texture Analysis ◽  
Buccal Mucosa ◽  
Ct Images ◽  
Oral Cancers

SU-E-J-203: Texture Analysis of 3D and 4D PET/CT Images of Lung Cancer

2013 ◽  
Vol 40 (6Part10) ◽  
pp. 198-198
Author(s):  
J Oliver ◽  
M Budzevich ◽  
G Zhang ◽  
K Latifi ◽  
C Kuykendall ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Texture Analysis ◽  
Ct Images ◽  
Pet Ct

Retrospective interactive rigid fusion of 18F-FDG-PET and CT

2006 ◽  
Vol 45 (02) ◽  
pp. 88-95 ◽  
Author(s):  
A. Nömayr ◽  
H. Greess ◽  
E. Fiedler ◽  
G. Platsch ◽  
B. Schuler-Thurner ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Image Fusion ◽  
Fdg Pet ◽  
False Negative ◽  
Spiral Ct ◽  
Ct Images ◽  
Pet Ct ◽  
Malignant Lesions ◽  
Ct Data

Summary Aim: This study investigates whether interactive rigid fusion of routine PET and CT data improves localization, detection and characterization of lesions compared to separate reading. For this purpose, routine PET and CT scans of patients with metastases from malignant melanoma were used. Patients, methods: In 34 patients with histologically confirmed malignant melanoma, FDG-PET and spiral CT were performed using clinical standard protocols. For all of these patients, gold standard was available. Clinical and radiological follow-up identified 82 lesions as definitely pathological. Two board-certified nuclear medicine physicians and two board-certified radiologists analyzed PET and CT images independently from each other. For each patient up to 32 anatomical regions (24 lymph node regions, 8 extranodular regions) were systematically classified. Discordant areas were interactively analyzed in manually and rigidly registered images using a commercially available fusion tool. No side-by-side reading was performed. Results: Image fusion disclosed that the evaluation of the PET images alone led to a mislocalization in 26 of 91 focally FDG enhancing lesions. The overall sensitivities of PET, CT, and image fusion were 85, 88, and 94%, respectively; the overall specificities of PET, CT and image fusion were 98, 95 and 100%, respectively. Image fusion exhibited statistically significant higher specificity values as compared with CT. Ten definitely malignant sites were false-negative in CT, but could be detected by PET. On the other hand, twelve metastases were false-negative in PET, but could be detected by CT. These included two lesions, which had a clear correlate on the PET image when the fused images were evaluated. On the whole, registration of the PET and CT images yielded additional diagnostic information in 44% of the definitely malignant lesions. Conclusion: Retrospective image fusion of independently obtained PET and CT data is particularly valuable in exactly localizing foci of abnormal FDG uptake and improves the detection of metastases of malignant melanoma.

Deep Supervised Residual U-Net for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT images

2019 ◽  
Author(s):  
Y Zhao ◽  
A Gafita ◽  
G Tetteh ◽  
L Xu ◽  
F Haupt ◽  
...  
Keyword(s):  
Ct Images ◽  
Psma Pet ◽  
Pet Ct
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