Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization

Desmond L Campbell,1 Hakmook Kang,2 Sepideh Shokouhi1 On behalf of The Alzheimer’s Disease Neuroimaging Initiative 1Department of Radiology and Radiological Sciences, 2Department of Biostatistics, Vanderbilt University Medical Center, Vanderbilt University Institute of Imaging S...

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Autores principales:	Campbell DL, Kang H, Shokouhi S
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2017
Materias:	Haralick Features Florbetapir Gray Level Co-occurrence Matrix Energy Entropy Geriatrics RC952-954.6
Acceso en línea:	https://doaj.org/article/56e21cd3e1004a6eb382faeb8556d3ea
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spelling	oai:doaj.org-article:56e21cd3e1004a6eb382faeb8556d3ea2021-12-02T06:54:39ZApplication of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization1178-1998https://doaj.org/article/56e21cd3e1004a6eb382faeb8556d3ea2017-12-01T00:00:00Zhttps://www.dovepress.com/application-of-haralick-texture-features-in-brain-18f-florbetapir-posi-peer-reviewed-article-CIAhttps://doaj.org/toc/1178-1998Desmond L Campbell,1 Hakmook Kang,2 Sepideh Shokouhi1 On behalf of The Alzheimer’s Disease Neuroimaging Initiative 1Department of Radiology and Radiological Sciences, 2Department of Biostatistics, Vanderbilt University Medical Center, Vanderbilt University Institute of Imaging Science, Nashville, TN, USA Objectives: Semi-quantitative image analysis methods in Alzheimer’s Disease (AD) require normalization of positron emission tomography (PET) images. However, recent studies have found variabilities associated with reference region selection of amyloid PET images. Haralick features (HFs) generated from the Gray Level Co-occurrence Matrix (GLCM) quantify spatial characteristics of amyloid PET radiotracer uptake without the need for intensity normalization. The objective of this study is to calculate several HFs in different diagnostic groups and determine the group differences.Methods: All image and metadata were acquired through the Alzheimer’s Disease Neuroimaging Initiative database. Subjects were grouped in three ways: by clinical diagnosis, by APOE e4 allele, and by Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-Cog) score. Several GLCM matrices were calculated for different direction and distances (1–4 mm) from multiple regions on PET images. The HFs, contrast, correlation, dissimilarity, energy, entropy, and homogeneity, were calculated from these GLCMs. Wilcoxon tests and Student t-tests were performed on Haralick features and standardized uptake value ratio (SUVR) values, respectively, to determine group differences. In addition to statistical testing, receiver operating characteristic (ROC) curves were generated to determine the discrimination performance of the selected regional HFs and the SUVR values.Results: Preliminary results from statistical testing indicate that HFs were capable of distinguishing groups at baseline and follow-up (false discovery rate corrected p<0.05) in particular regions at much higher occurrences than SUVR (81 of 252). Conversely, we observed nearly no significant differences between all groups within ROIs at baseline or follow-up utilizing SUVR. From the ROC analysis, we found that the Energy and Entropy offered the best performance to distinguish Normal versus mild cognitive impairment and ADAS-Cog negative versus ADAS-Cog positive groups.Conclusion: These results suggest that this technique could improve subject stratification in AD drug trials and help to evaluate the disease progression and treatment effects longitudinally without the disadvantages associated with intensity normalization. Keywords: Haralick features, florbetapir, gray level co-occurrence matrix, energy, entropyCampbell DLKang HShokouhi SDove Medical PressarticleHaralick FeaturesFlorbetapirGray Level Co-occurrence MatrixEnergyEntropyGeriatricsRC952-954.6ENClinical Interventions in Aging, Vol Volume 12, Pp 2077-2086 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Haralick Features Florbetapir Gray Level Co-occurrence Matrix Energy Entropy Geriatrics RC952-954.6
spellingShingle	Haralick Features Florbetapir Gray Level Co-occurrence Matrix Energy Entropy Geriatrics RC952-954.6 Campbell DL Kang H Shokouhi S Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization
description	Desmond L Campbell,1 Hakmook Kang,2 Sepideh Shokouhi1 On behalf of The Alzheimer’s Disease Neuroimaging Initiative 1Department of Radiology and Radiological Sciences, 2Department of Biostatistics, Vanderbilt University Medical Center, Vanderbilt University Institute of Imaging Science, Nashville, TN, USA Objectives: Semi-quantitative image analysis methods in Alzheimer’s Disease (AD) require normalization of positron emission tomography (PET) images. However, recent studies have found variabilities associated with reference region selection of amyloid PET images. Haralick features (HFs) generated from the Gray Level Co-occurrence Matrix (GLCM) quantify spatial characteristics of amyloid PET radiotracer uptake without the need for intensity normalization. The objective of this study is to calculate several HFs in different diagnostic groups and determine the group differences.Methods: All image and metadata were acquired through the Alzheimer’s Disease Neuroimaging Initiative database. Subjects were grouped in three ways: by clinical diagnosis, by APOE e4 allele, and by Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-Cog) score. Several GLCM matrices were calculated for different direction and distances (1–4 mm) from multiple regions on PET images. The HFs, contrast, correlation, dissimilarity, energy, entropy, and homogeneity, were calculated from these GLCMs. Wilcoxon tests and Student t-tests were performed on Haralick features and standardized uptake value ratio (SUVR) values, respectively, to determine group differences. In addition to statistical testing, receiver operating characteristic (ROC) curves were generated to determine the discrimination performance of the selected regional HFs and the SUVR values.Results: Preliminary results from statistical testing indicate that HFs were capable of distinguishing groups at baseline and follow-up (false discovery rate corrected p<0.05) in particular regions at much higher occurrences than SUVR (81 of 252). Conversely, we observed nearly no significant differences between all groups within ROIs at baseline or follow-up utilizing SUVR. From the ROC analysis, we found that the Energy and Entropy offered the best performance to distinguish Normal versus mild cognitive impairment and ADAS-Cog negative versus ADAS-Cog positive groups.Conclusion: These results suggest that this technique could improve subject stratification in AD drug trials and help to evaluate the disease progression and treatment effects longitudinally without the disadvantages associated with intensity normalization. Keywords: Haralick features, florbetapir, gray level co-occurrence matrix, energy, entropy
format	article
author	Campbell DL Kang H Shokouhi S
author_facet	Campbell DL Kang H Shokouhi S
author_sort	Campbell DL
title	Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization
title_short	Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization
title_full	Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization
title_fullStr	Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization
title_full_unstemmed	Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization
title_sort	application of haralick texture features in brain [18f]-florbetapir positron emission tomography without reference region normalization
publisher	Dove Medical Press
publishDate	2017
url	https://doaj.org/article/56e21cd3e1004a6eb382faeb8556d3ea
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_version_	1718399691517853696

Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization

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