scholarly journals Intensity normalization methods in brain FDG-PET quantification

NeuroImage ◽  
2020 ◽  
Vol 222 ◽  
pp. 117229 ◽  
Author(s):  
Francisco J. López-González ◽  
Jesús Silva-Rodríguez ◽  
José Paredes-Pacheco ◽  
Aida Niñerola-Baizán ◽  
Nikos Efthimiou ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Pet Quantification ◽  
Normalization Methods ◽  
Intensity Normalization

scholarly journals Circadian rhythm impacts preclinical FDG-PET quantification in the brain, but not in xenograft tumors

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marcel A. Krueger ◽  
Carsten Calaminus ◽  
Julia Schmitt ◽  
Bernd J. Pichler
Keyword(s):  
Circadian Rhythm ◽  
Fdg Pet ◽  
Pet Quantification ◽  
The Brain

scholarly journals P1.05-057 Prediction of Early Recurrence in Patients with Stage I and II Non-Small Cell Lung Cancer Using FDG PET Quantification

2017 ◽  
Vol 12 (1) ◽  
pp. S649-S650
Author(s):  
Michael Arvanitakis ◽  
Irene Burger ◽  
Seraina Steiger ◽  
Beate Sick ◽  
Walter Weder ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Fdg Pet ◽  
Early Recurrence ◽  
Small Cell ◽  
Stage I ◽  
Small Cell Lung ◽  
Pet Quantification

P2-413: THE MIDBRAIN IS THE OPTIMAL INTENSITY NORMALIZATION REGION FOR FDG-PET STUDIES OF NORMAL AGING

2006 ◽  
Vol 14 (7S_Part_16) ◽  
pp. P866-P866
Author(s):  
Scott Nugent ◽  
Etienne Croteau ◽  
Olivier Potvin ◽  
Christian-Alexandre Castellano ◽  
Stephen Cunnane ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Normal Aging ◽  
Optimal Intensity ◽  
Intensity Normalization

scholarly journals Measurement of Face Detection Accuracy Using Intensity Normalization Method and Homomorphic Filtering

2017 ◽  
Vol 2 (1) ◽  
pp. 107
Author(s):  
I Nyoman Gede Arya Astawa ◽  
I Ketut Gede Darma Putra ◽  
I Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Recognition System ◽  
Face Image ◽  
Normalization Method ◽  
Detection Accuracy ◽  
Normalization Methods ◽  
Homomorphic Filtering ◽  
The Face ◽  
Intensity Normalization

One of the factors that affects the detection system or face recognition is lighting. Image color processing can help the face recognition system in poor lighting conditions. In this study, homomorphic filtering and intensity normalization methods used to help improve the accuracy of face image detection. The experimental results show that the non-uniform of the illumination of the face image can be uniformed using the intensity normalization method with the average value of Peak Signal to Noise Ratio (PSNR) obtained from the whole experiment is 22.05314 and the average Absolute Mean Brightness Error (AMBE) value obtained is 6.147787. The results showed that homomorphic filtering and intensity normalization methods can be used to improve the detection accuracy of a face image.

scholarly journals Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and Alzheimer’s disease

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Scott Nugent ◽  
Etienne Croteau ◽  
Olivier Potvin ◽  
Christian-Alexandre Castellano ◽  
Louis Dieumegarde ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fdg Pet ◽  
Normal Aging ◽  
Optimal Intensity ◽  
Intensity Normalization ◽  
Selection Of

scholarly journals Correction: Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0135107 ◽  
Author(s):  
A. Brahim ◽  
J. Ramírez ◽  
J. M. Górriz ◽  
L. Khedher ◽  
D. Salas-Gonzalez
Keyword(s):  
Automatic Detection ◽  
Normalization Methods ◽  
Intensity Normalization

scholarly journals Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients

2015 ◽  
Vol 42 (13) ◽  
pp. 2072-2082 ◽  
Author(s):  
Elske Quak ◽  
Pierre-Yves Le Roux ◽  
Michael S. Hofman ◽  
Philippe Robin ◽  
David Bourhis ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Lesion Detection ◽  
Oncology Patients ◽  
Pet Quantification

scholarly journals The Pons As Region of Reference For Intensity Normalization In Semi-Quantitative Analysis of Brain 18FDG PET: Application to Metabolic Changes Related to Ageing in Conventional and Digital Control Databases

2021 ◽  
Author(s):  
Antoine Verger ◽  
Matthieu Doyen ◽  
Jacques-Yves Campion ◽  
Eric Guedj
Keyword(s):  
Fdg Pet ◽  
Correlation Coefficients ◽  
Metabolic Changes ◽  
18Fdg Pet ◽  
Median Correlation ◽  
Pet Ct ◽  
Digital Pet ◽  
Fdg Pet Ct ◽  
Intensity Normalization ◽  
Two Populations

Abstract Background: To define the most appropriate region for intensity normalization in brain 18FDG PET analysis through ageing.Brain metabolic changes related to ageing were evaluated in two populations of healthy controls who underwent conventional (n=56) or digital (n=78) 18FDG PET/CT. The median correlation coefficients between age and the metabolism of each 120 atlas brain region were reported for 120 distinct intensity normalizations (according to the 120 regions). SPM linear regression analyses with age were performed on most significant normalizations (FWE, p<0.05).Results: The cerebellum and pons were the two sole regions showing median coefficients of correlation with age less than -0.5. With SPM, the intensity normalization through the pons provided at least 1.7- and 2.5-fold more significant cluster volumes than other normalizations for conventional and digital PET respectively. Conclusions: The pons is the most appropriate area for brain 18FDG PET intensity normalization for examining the metabolic changes through ageing.

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