Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

Abstract In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus rega...

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Autores principales:	Patrik Brynolfsson, David Nilsson, Turid Torheim, Thomas Asklund, Camilla Thellenberg Karlsson, Johan Trygg, Tufve Nyholm, Anders Garpebring
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/74ba0e7b0d094daf9ac8f49b66e5359c
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spelling	oai:doaj.org-article:74ba0e7b0d094daf9ac8f49b66e5359c2021-12-02T12:32:25ZHaralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters10.1038/s41598-017-04151-42045-2322https://doaj.org/article/74ba0e7b0d094daf9ac8f49b66e5359c2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04151-4https://doaj.org/toc/2045-2322Abstract In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.Patrik BrynolfssonDavid NilssonTurid TorheimThomas AsklundCamilla Thellenberg KarlssonJohan TryggTufve NyholmAnders GarpebringNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Patrik Brynolfsson David Nilsson Turid Torheim Thomas Asklund Camilla Thellenberg Karlsson Johan Trygg Tufve Nyholm Anders Garpebring Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
description	Abstract In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.
format	article
author	Patrik Brynolfsson David Nilsson Turid Torheim Thomas Asklund Camilla Thellenberg Karlsson Johan Trygg Tufve Nyholm Anders Garpebring
author_facet	Patrik Brynolfsson David Nilsson Turid Torheim Thomas Asklund Camilla Thellenberg Karlsson Johan Trygg Tufve Nyholm Anders Garpebring
author_sort	Patrik Brynolfsson
title	Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
title_short	Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
title_full	Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
title_fullStr	Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
title_full_unstemmed	Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
title_sort	haralick texture features from apparent diffusion coefficient (adc) mri images depend on imaging and pre-processing parameters
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/74ba0e7b0d094daf9ac8f49b66e5359c
work_keys_str_mv	AT patrikbrynolfsson haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters AT davidnilsson haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters AT turidtorheim haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters AT thomasasklund haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters AT camillathellenbergkarlsson haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters AT johantrygg haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters AT tufvenyholm haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters AT andersgarpebring haralicktexturefeaturesfromapparentdiffusioncoefficientadcmriimagesdependonimagingandpreprocessingparameters
_version_	1718394102247063552

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

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