Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
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 tex...
Saved in:
| Published in | Scientific reports Vol. 7; no. 1; pp. 4041 - 11 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
22.06.2017
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-017-04151-4 |
Cover
Loading…
| 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. |
|---|---|
| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 4041 |
| Author | Brynolfsson, Patrik Nilsson, David Karlsson, Camilla Thellenberg Torheim, Turid Garpebring, Anders Nyholm, Tufve Asklund, Thomas Trygg, Johan |
| Author_xml | – sequence: 1 givenname: Patrik surname: Brynolfsson fullname: Brynolfsson, Patrik email: patrik.brynolfsson@umu.se organization: Dept. of Radiation Sciences, Umeå University – sequence: 2 givenname: David surname: Nilsson fullname: Nilsson, David organization: Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University – sequence: 3 givenname: Turid orcidid: 0000-0001-6191-2036 surname: Torheim fullname: Torheim, Turid organization: Cancer Research UK Cambridge Institute, University of Cambridge – sequence: 4 givenname: Thomas surname: Asklund fullname: Asklund, Thomas organization: Dept. of Radiation Sciences, Umeå University – sequence: 5 givenname: Camilla Thellenberg surname: Karlsson fullname: Karlsson, Camilla Thellenberg organization: Dept. of Radiation Sciences, Umeå University – sequence: 6 givenname: Johan surname: Trygg fullname: Trygg, Johan organization: Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University – sequence: 7 givenname: Tufve surname: Nyholm fullname: Nyholm, Tufve organization: Dept. of Radiation Sciences, Umeå University – sequence: 8 givenname: Anders surname: Garpebring fullname: Garpebring, Anders organization: Dept. of Radiation Sciences, Umeå University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28642480$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-134993$$DView record from Swedish Publication Index |
| BookMark | eNp9kk9v1DAQxSNUREvpF-CAInEph4Dt2Fn7grTaAl2pCAkBV8uxJ4uXbJzaCX8ufHYmu0u1Wwl8sTP-vZfx6D3OTrrQQZY9peQlJaV8lTgVShaEzgqCR1rwB9kZI1wUrGTs5OB8ml2ktCa4BFOcqkfZKZMVZ1ySs-z3tYmm9fZbPsDPYYyQN2CmPeVNDJvc9L2J0A25800zJh-63AZoGm_9VL2cXy1e5O8_LnO_MSsUOeihczliU8F3q9zgZx-h6GOwkNJUQkuzgQFiepI9bEyb4GK_n2ef3775tLgubj68Wy7mN4WtOB8KVjtGDaGE1cIR1zS0nrnaVozUpHYKB2GAS4n3paqE4I5QyYSxjgvHLYHyPFvufF0wa91HbC7-0sF4vS2EuNImDt62oGe8NgRmNXFEcWcaZaxsuKqrCkQplEWvYueVfkA_1kduV_7LfOs2bkZNS65UifzrHY_wBpzFueHIj2THN53_qlfhuxZcUi44GlzuDWK4HSENeuOThbY1HYQxaapoWSrOBUH0-T10HcbY4WiREkJVsiISqWeHHd218jcWCLAdYGNIKUJzh1Cip_jpXfw0xk9v46enNuU9kfWDGTAy-Crf_l9a7keK_-lWEA_a_rfqDxdW774 |
| CitedBy_id | crossref_primary_10_3390_electronics9050767 crossref_primary_10_1002_advs_202105396 crossref_primary_10_2214_AJR_18_20624 crossref_primary_10_1007_s00198_019_04924_9 crossref_primary_10_3390_rs10111690 crossref_primary_10_1088_1361_6560_ace305 crossref_primary_10_1186_s42492_019_0025_6 crossref_primary_10_3390_jfb15010024 crossref_primary_10_1088_1361_6560_aad8ec crossref_primary_10_1007_s40846_021_00671_7 crossref_primary_10_3390_inventions9010008 crossref_primary_10_3390_axioms12020197 crossref_primary_10_1016_j_modpat_2023_100199 crossref_primary_10_46300_91015_2022_16_14 crossref_primary_10_1038_s41467_019_08893_9 crossref_primary_10_1016_j_canrad_2019_08_002 crossref_primary_10_3390_s24144472 crossref_primary_10_1097_RCT_0000000000000851 crossref_primary_10_3390_biom13020249 crossref_primary_10_1021_acsomega_2c02103 crossref_primary_10_1097_RCT_0000000000000731 crossref_primary_10_3390_diagnostics13132140 crossref_primary_10_3390_pr12122837 crossref_primary_10_1016_j_indcrop_2020_113223 crossref_primary_10_1109_JBHI_2019_2956351 crossref_primary_10_1093_braincomms_fcad195 crossref_primary_10_1002_cne_25105 crossref_primary_10_1016_j_matchar_2018_08_009 crossref_primary_10_3389_fonc_2021_708398 crossref_primary_10_1016_j_ejmp_2020_10_029 crossref_primary_10_32604_cmc_2022_024085 crossref_primary_10_1007_s00066_018_1402_3 crossref_primary_10_1007_s00261_018_1832_5 crossref_primary_10_1038_s41598_019_48738_5 crossref_primary_10_1038_s44220_023_00168_z crossref_primary_10_1088_1361_6560_acf183 crossref_primary_10_1038_s41398_022_02242_z crossref_primary_10_3390_agronomy13081974 crossref_primary_10_1080_22797254_2022_2161420 crossref_primary_10_3390_foods13050697 crossref_primary_10_1093_mam_ozae037 crossref_primary_10_1016_j_phro_2021_01_004 crossref_primary_10_1038_s41598_022_19009_7 crossref_primary_10_1002_mrm_28592 crossref_primary_10_1002_acm2_12482 crossref_primary_10_3390_cancers16152668 crossref_primary_10_1093_rasti_rzae022 crossref_primary_10_3390_bioengineering10010080 crossref_primary_10_1186_s40644_024_00674_y crossref_primary_10_1016_j_rsase_2023_100938 crossref_primary_10_1002_mp_14173 crossref_primary_10_1364_BOE_10_000914 crossref_primary_10_1007_s10278_024_01303_7 crossref_primary_10_1016_j_brachy_2024_08_256 crossref_primary_10_1016_j_bspc_2021_102695 crossref_primary_10_1016_j_commatsci_2019_01_006 crossref_primary_10_2174_1573405614666181012102626 crossref_primary_10_1038_s41598_022_13937_0 crossref_primary_10_1007_s11837_022_05265_5 crossref_primary_10_1097_RLI_0000000000001026 crossref_primary_10_1038_s41598_022_23838_x crossref_primary_10_3389_fonc_2022_852308 crossref_primary_10_1097_RCT_0000000000001344 crossref_primary_10_1088_1361_6560_acb885 crossref_primary_10_1007_s11042_023_17385_4 crossref_primary_10_1016_j_isprsjprs_2020_09_013 crossref_primary_10_1038_s41598_022_15364_7 crossref_primary_10_1007_s11227_020_03565_8 crossref_primary_10_14316_pmp_2022_33_4_158 crossref_primary_10_1002_jmri_28190 crossref_primary_10_1177_02841851231216555 crossref_primary_10_1002_jmri_28191 crossref_primary_10_1155_2022_5336373 crossref_primary_10_3390_app122111269 crossref_primary_10_3390_bioengineering12010047 crossref_primary_10_3390_rs14174132 crossref_primary_10_3390_app122010461 crossref_primary_10_1002_jmri_26883 crossref_primary_10_3389_fpls_2020_01275 crossref_primary_10_3390_tomography9020058 crossref_primary_10_1016_j_heliyon_2024_e26192 crossref_primary_10_3390_polym15173544 crossref_primary_10_3390_jcm12072605 crossref_primary_10_3389_fpls_2024_1492372 crossref_primary_10_1186_s41747_020_00199_6 crossref_primary_10_1088_1361_6560_aa884d crossref_primary_10_1055_a_1079_3855 crossref_primary_10_1007_s00330_023_10164_7 crossref_primary_10_1097_RCT_0000000000001088 crossref_primary_10_1016_j_bspc_2022_103571 crossref_primary_10_1117_1_JMI_6_2_024502 crossref_primary_10_1109_TUFFC_2022_3161719 crossref_primary_10_3390_agronomy10111642 crossref_primary_10_1200_CCI_21_00156 crossref_primary_10_1093_braincomms_fcac116 crossref_primary_10_1038_s41598_021_94826_w crossref_primary_10_1007_s42979_024_03109_4 crossref_primary_10_1016_j_asoc_2025_112767 crossref_primary_10_1117_1_JMI_9_4_044005 crossref_primary_10_4103_jmp_jmp_149_24 crossref_primary_10_4103_jmss_jmss_50_22 |
| Cites_doi | 10.1088/0031-9155/56/6/006 10.2214/AJR.14.12570 10.1097/RLI.0b013e3181a50a66 10.1158/1078-0432.CCR-14-0990 10.1148/radiology.161.2.3763909 10.1109/36.752194 10.1002/mrm.10308 10.1016/j.chemolab.2004.02.005 10.1186/1471-2407-14-366 10.1118/1.4894812 10.1016/j.patrec.2006.05.008 10.2214/AJR.14.13825 10.1161/01.CIR.68.4.834 10.1016/0301-5629(79)90004-8 10.5589/m02-004 10.1056/NEJMoa043330 10.1002/mrm.1910340618 10.1007/s00330-015-3701-8 10.1063/1.1695690 10.1593/neo.81328 10.1109/TMI.2012.2206398 10.2967/jnumed.110.082404 10.1186/1475-925X-9-60 10.1109/TMI.2014.2321024 10.2214/AJR.14.13279 10.1109/TSMC.1973.4309314 10.1118/1.3081408 10.1038/srep11075 10.1088/0031-9155/60/7/2685 10.1214/aoms/1177730256 10.1371/journal.pone.0108335 10.1001/jama.280.11.969 10.31887/DCNS.2004.6.2/amaterka 10.1515/fcds-2015-0011 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2017 Copyright Nature Publishing Group Jun 2017 |
| Copyright_xml | – notice: The Author(s) 2017 – notice: Copyright Nature Publishing Group Jun 2017 |
| DBID | C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM ADHXS ADTPV AOWAS D8T D93 ZZAVC DOA |
| DOI | 10.1038/s41598-017-04151-4 |
| DatabaseName | SpringerOpen Free (Free internet resource, activated by CARLI) CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni) Medical Database Science Database (ProQuest) Biological Science Database (ProQuest) ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) SWEPUB Umeå universitet full text SwePub SwePub Articles SWEPUB Freely available online SWEPUB Umeå universitet SwePub Articles full text DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database CrossRef PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Open Access url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: 开放获取期刊（Open Access Journals） url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 11 |
| ExternalDocumentID | oai_doaj_org_article_74ba0e7b0d094daf9ac8f49b66e5359c oai_DiVA_org_umu_134993 PMC5481454 28642480 10_1038_s41598_017_04151_4 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS EJD ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFPKN CITATION PHGZM PHGZT NPM 7XB 8FK AARCD K9. PJZUB PKEHL PPXIY PQEST PQGLB PQUKI PRINS Q9U 7X8 5PM ADHXS ADTPV AOWAS D8T D93 IPNFZ RIG ZZAVC PUEGO |
| ID | FETCH-LOGICAL-c644t-2bd21a0102b5d0dff1b7dbc620b0bd9598ae488102396554d01825acd45d4c0e3 |
| IEDL.DBID | M48 |
| ISSN | 2045-2322 |
| IngestDate | Wed Aug 27 01:28:33 EDT 2025 Thu Aug 21 06:29:56 EDT 2025 Thu Aug 21 18:30:58 EDT 2025 Tue Aug 05 09:28:44 EDT 2025 Wed Aug 13 06:43:03 EDT 2025 Thu Apr 03 06:57:02 EDT 2025 Tue Jul 01 01:33:08 EDT 2025 Thu Apr 24 23:10:02 EDT 2025 Fri Feb 21 02:39:21 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c644t-2bd21a0102b5d0dff1b7dbc620b0bd9598ae488102396554d01825acd45d4c0e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-6191-2036 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1038/s41598-017-04151-4 |
| PMID | 28642480 |
| PQID | 1955968608 |
| PQPubID | 2041939 |
| PageCount | 11 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_74ba0e7b0d094daf9ac8f49b66e5359c swepub_primary_oai_DiVA_org_umu_134993 pubmedcentral_primary_oai_pubmedcentral_nih_gov_5481454 proquest_miscellaneous_1913394450 proquest_journals_1955968608 pubmed_primary_28642480 crossref_primary_10_1038_s41598_017_04151_4 crossref_citationtrail_10_1038_s41598_017_04151_4 springer_journals_10_1038_s41598_017_04151_4 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2017-06-22 |
| PublicationDateYYYYMMDD | 2017-06-22 |
| PublicationDate_xml | – month: 06 year: 2017 text: 2017-06-22 day: 22 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2017 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | O’Connor (CR5) 2015; 21 Brynolfsson (CR7) 2014; 41 Tixier (CR12) 2011; 52 Savio (CR18) 2010; 9 Schieda (CR9) 2015; 204 Gómez, Pereira, Infantosi (CR15) 2012; 31 Torheim (CR8) 2014; 33 Lerski (CR3) 1979; 5 Mayerhoefer (CR20) 2009; 44 CR35 Arivazhagan, Ganesan, Priyal (CR10) 2006; 27 Wibmer (CR24) 2015; 25 Le Bihan (CR28) 1986; 161 CR34 CR31 Haralick, Shanmugam, Dinstein (CR1) 1973; 3 Cai, Liu, Peng, Wu, Li (CR26) 2014; 14 Stejskal, Tanner (CR27) 1965; 42 Leijenaar (CR13) 2015; 5 Mayerhoefer, Szomolanyi, Jirak, Materka, Trattnig (CR19) 2009; 36 Soh, Tsatsoulis (CR17) 1999; 37 Stupp (CR30) 2005; 352 Gudbjartsson, Patz (CR33) 1995; 34 Bharati, Liu, MacGregor (CR11) 2004; 72 Reese, Heid, Weisskoff, Wedeen (CR32) 2003; 49 CR23 CR22 CR21 Padhani (CR29) 2009; 11 Materka (CR2) 2004; 6 Kim, Kim, Moon, Yoon, Kwak (CR14) 2015; 205 Kierans (CR25) 2014; 203 Alic (CR6) 2011; 56 Skorton, Collins, Woskoff, Bean, Melton (CR4) 1983; 68 Clausi (CR16) 2002; 28 L-K Soh (4151_CR17) 1999; 37 ME Mayerhoefer (4151_CR19) 2009; 36 JPB O’Connor (4151_CR5) 2015; 21 R Lerski (4151_CR3) 1979; 5 D Le Bihan (4151_CR28) 1986; 161 A Materka (4151_CR2) 2004; 6 ME Mayerhoefer (4151_CR20) 2009; 44 AS Kierans (4151_CR25) 2014; 203 A Wibmer (4151_CR24) 2015; 25 4151_CR31 4151_CR34 DJ Skorton (4151_CR4) 1983; 68 T Torheim (4151_CR8) 2014; 33 4151_CR35 H Cai (4151_CR26) 2014; 14 RT Leijenaar (4151_CR13) 2015; 5 W Gómez (4151_CR15) 2012; 31 EO Stejskal (4151_CR27) 1965; 42 TG Reese (4151_CR32) 2003; 49 P Brynolfsson (4151_CR7) 2014; 41 SJ Savio (4151_CR18) 2010; 9 H Gudbjartsson (4151_CR33) 1995; 34 L Alic (4151_CR6) 2011; 56 SY Kim (4151_CR14) 2015; 205 MH Bharati (4151_CR11) 2004; 72 S Arivazhagan (4151_CR10) 2006; 27 4151_CR23 4151_CR21 RM Haralick (4151_CR1) 1973; 3 4151_CR22 F Tixier (4151_CR12) 2011; 52 AR Padhani (4151_CR29) 2009; 11 N Schieda (4151_CR9) 2015; 204 R Stupp (4151_CR30) 2005; 352 DA Clausi (4151_CR16) 2002; 28 |
| References_xml | – ident: CR22 – volume: 56 start-page: 1601 year: 2011 end-page: 16 ident: CR6 article-title: Heterogeneity in DCE-MRI parametric maps: a biomarker for treatment response? publication-title: Physics in medicine and biology doi: 10.1088/0031-9155/56/6/006 – volume: 203 start-page: W637 year: 2014 end-page: W44 ident: CR25 article-title: Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma publication-title: AJR. American journal of roentgenology doi: 10.2214/AJR.14.12570 – volume: 44 start-page: 405 year: 2009 end-page: 411 ident: CR20 article-title: Effects of magnetic resonance image interpolation on the results of texture-based pattern classification: a phantom study publication-title: Invest Radiol doi: 10.1097/RLI.0b013e3181a50a66 – volume: 21 start-page: 249 year: 2015 end-page: 257 ident: CR5 article-title: Imaging intratumor heterogeneity: Role in therapy response, resistance, and clinical outcome publication-title: Clinical Cancer Research doi: 10.1158/1078-0432.CCR-14-0990 – volume: 161 start-page: 401 year: 1986 end-page: 407 ident: CR28 article-title: MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders publication-title: Radiology doi: 10.1148/radiology.161.2.3763909 – volume: 37 start-page: 780 year: 1999 end-page: 795 ident: CR17 article-title: Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurence Matrices publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/36.752194 – volume: 49 start-page: 177 year: 2003 end-page: 82 ident: CR32 article-title: Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo publication-title: Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine doi: 10.1002/mrm.10308 – volume: 72 start-page: 57 year: 2004 end-page: 71 ident: CR11 article-title: Image texture analysis: Methods and comparisons publication-title: Chemometrics and Intelligent Laboratory Systems doi: 10.1016/j.chemolab.2004.02.005 – volume: 14 start-page: 366 year: 2014 ident: CR26 article-title: Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols publication-title: BMC cancer doi: 10.1186/1471-2407-14-366 – ident: CR35 – volume: 41 start-page: 101903 year: 2014 ident: CR7 article-title: ADC texture - An imaging biomarker for high-grade glioma? publication-title: Medical Physics doi: 10.1118/1.4894812 – volume: 27 start-page: 1976 year: 2006 end-page: 1982 ident: CR10 article-title: Texture classification using Gabor wavelets based rotation invariant features publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2006.05.008 – volume: 205 start-page: 343 year: 2015 end-page: 51 ident: CR14 article-title: Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography publication-title: AJR Am J Roentgenol doi: 10.2214/AJR.14.13825 – ident: CR23 – volume: 68 start-page: 834 year: 1983 end-page: 840 ident: CR4 article-title: Range- and azimuth-dependent variability of image texture in two- dimensional echocardiograms publication-title: Circulation doi: 10.1161/01.CIR.68.4.834 – volume: 5 start-page: 341 year: 1979 end-page: 343 ident: CR3 article-title: Computer analysis of ultrasonic signals in diffuse liver disease publication-title: Ultrasound in Medicine & Biology doi: 10.1016/0301-5629(79)90004-8 – ident: CR21 – volume: 28 start-page: 45 year: 2002 end-page: 62 ident: CR16 article-title: An analysis of co-occurrence texture statistics as a function of grey level quantization publication-title: Canadian Journal of Remote Sensing doi: 10.5589/m02-004 – volume: 352 start-page: 987 year: 2005 end-page: 996 ident: CR30 article-title: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma publication-title: The New England journal of medicine doi: 10.1056/NEJMoa043330 – volume: 34 start-page: 910 year: 1995 end-page: 914 ident: CR33 article-title: The Rician distribution of noisy MRI data publication-title: Magnetic Resonance in Medicine doi: 10.1002/mrm.1910340618 – volume: 25 start-page: 2840 year: 2015 end-page: 2850 ident: CR24 article-title: Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores publication-title: European Radiology doi: 10.1007/s00330-015-3701-8 – volume: 42 start-page: 288 year: 1965 ident: CR27 article-title: Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient publication-title: The Journal of Chemical Physics doi: 10.1063/1.1695690 – volume: 6 start-page: 243 year: 2004 end-page: 250 ident: CR2 article-title: Texture analysis methodologies for magnetic resonance imaging publication-title: Dialogues in clinical neuroscience – volume: 11 start-page: 102 year: 2009 end-page: 125 ident: CR29 article-title: Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations publication-title: Neoplasia (New York, N.Y.) doi: 10.1593/neo.81328 – volume: 31 start-page: 1889 year: 2012 end-page: 1899 ident: CR15 article-title: Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2012.2206398 – ident: CR31 – volume: 52 start-page: 369 year: 2011 end-page: 78 ident: CR12 article-title: Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer publication-title: Journal of nuclear medicine: official publication, Society of Nuclear Medicine doi: 10.2967/jnumed.110.082404 – volume: 9 start-page: 60 year: 2010 ident: CR18 article-title: Effect of slice thickness on brain magnetic resonance image texture analysis publication-title: Biomedical engineering online doi: 10.1186/1475-925X-9-60 – volume: 33 start-page: 1648 year: 2014 end-page: 1656 ident: CR8 article-title: Classi fi cation of Dynamic Contrast Enhanced MR Images of Cervical Cancers Using Texture Analysis and Support Vector Machines publication-title: IEEE transactions on medical imaging doi: 10.1109/TMI.2014.2321024 – ident: CR34 – volume: 204 start-page: 1013 year: 2015 end-page: 1023 ident: CR9 article-title: Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis publication-title: American Journal of Roentgenology doi: 10.2214/AJR.14.13279 – volume: 3 start-page: 610 year: 1973 end-page: 621 ident: CR1 article-title: Textural Features for Image Classification publication-title: IEEE Transactions on Systems, Man, and Cybernetics doi: 10.1109/TSMC.1973.4309314 – volume: 36 start-page: 1236 year: 2009 end-page: 1243 ident: CR19 article-title: Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study publication-title: Medical physics doi: 10.1118/1.3081408 – volume: 5 start-page: 11075 year: 2015 ident: CR13 article-title: The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis publication-title: Scientific Reports doi: 10.1038/srep11075 – volume: 68 start-page: 834 year: 1983 ident: 4151_CR4 publication-title: Circulation doi: 10.1161/01.CIR.68.4.834 – ident: 4151_CR23 doi: 10.1088/0031-9155/60/7/2685 – volume: 56 start-page: 1601 year: 2011 ident: 4151_CR6 publication-title: Physics in medicine and biology doi: 10.1088/0031-9155/56/6/006 – volume: 27 start-page: 1976 year: 2006 ident: 4151_CR10 publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2006.05.008 – volume: 5 start-page: 11075 year: 2015 ident: 4151_CR13 publication-title: Scientific Reports doi: 10.1038/srep11075 – volume: 41 start-page: 101903 year: 2014 ident: 4151_CR7 publication-title: Medical Physics doi: 10.1118/1.4894812 – volume: 203 start-page: W637 year: 2014 ident: 4151_CR25 publication-title: AJR. American journal of roentgenology doi: 10.2214/AJR.14.12570 – ident: 4151_CR35 doi: 10.1214/aoms/1177730256 – ident: 4151_CR22 doi: 10.1371/journal.pone.0108335 – volume: 42 start-page: 288 year: 1965 ident: 4151_CR27 publication-title: The Journal of Chemical Physics doi: 10.1063/1.1695690 – volume: 72 start-page: 57 year: 2004 ident: 4151_CR11 publication-title: Chemometrics and Intelligent Laboratory Systems doi: 10.1016/j.chemolab.2004.02.005 – volume: 33 start-page: 1648 year: 2014 ident: 4151_CR8 publication-title: IEEE transactions on medical imaging doi: 10.1109/TMI.2014.2321024 – volume: 9 start-page: 60 year: 2010 ident: 4151_CR18 publication-title: Biomedical engineering online doi: 10.1186/1475-925X-9-60 – volume: 44 start-page: 405 year: 2009 ident: 4151_CR20 publication-title: Invest Radiol doi: 10.1097/RLI.0b013e3181a50a66 – volume: 5 start-page: 341 year: 1979 ident: 4151_CR3 publication-title: Ultrasound in Medicine & Biology doi: 10.1016/0301-5629(79)90004-8 – ident: 4151_CR31 doi: 10.1001/jama.280.11.969 – volume: 37 start-page: 780 year: 1999 ident: 4151_CR17 publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/36.752194 – volume: 21 start-page: 249 year: 2015 ident: 4151_CR5 publication-title: Clinical Cancer Research doi: 10.1158/1078-0432.CCR-14-0990 – volume: 6 start-page: 243 year: 2004 ident: 4151_CR2 publication-title: Dialogues in clinical neuroscience doi: 10.31887/DCNS.2004.6.2/amaterka – volume: 205 start-page: 343 year: 2015 ident: 4151_CR14 publication-title: AJR Am J Roentgenol doi: 10.2214/AJR.14.13825 – volume: 25 start-page: 2840 year: 2015 ident: 4151_CR24 publication-title: European Radiology doi: 10.1007/s00330-015-3701-8 – volume: 28 start-page: 45 year: 2002 ident: 4151_CR16 publication-title: Canadian Journal of Remote Sensing doi: 10.5589/m02-004 – volume: 352 start-page: 987 year: 2005 ident: 4151_CR30 publication-title: The New England journal of medicine doi: 10.1056/NEJMoa043330 – volume: 204 start-page: 1013 year: 2015 ident: 4151_CR9 publication-title: American Journal of Roentgenology doi: 10.2214/AJR.14.13279 – ident: 4151_CR34 – volume: 3 start-page: 610 year: 1973 ident: 4151_CR1 publication-title: IEEE Transactions on Systems, Man, and Cybernetics doi: 10.1109/TSMC.1973.4309314 – volume: 31 start-page: 1889 year: 2012 ident: 4151_CR15 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2012.2206398 – volume: 36 start-page: 1236 year: 2009 ident: 4151_CR19 publication-title: Medical physics doi: 10.1118/1.3081408 – volume: 34 start-page: 910 year: 1995 ident: 4151_CR33 publication-title: Magnetic Resonance in Medicine doi: 10.1002/mrm.1910340618 – volume: 161 start-page: 401 year: 1986 ident: 4151_CR28 publication-title: Radiology doi: 10.1148/radiology.161.2.3763909 – volume: 11 start-page: 102 year: 2009 ident: 4151_CR29 publication-title: Neoplasia (New York, N.Y.) doi: 10.1593/neo.81328 – ident: 4151_CR21 doi: 10.1515/fcds-2015-0011 – volume: 49 start-page: 177 year: 2003 ident: 4151_CR32 publication-title: Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine doi: 10.1002/mrm.10308 – volume: 52 start-page: 369 year: 2011 ident: 4151_CR12 publication-title: Journal of nuclear medicine: official publication, Society of Nuclear Medicine doi: 10.2967/jnumed.110.082404 – volume: 14 start-page: 366 year: 2014 ident: 4151_CR26 publication-title: BMC cancer doi: 10.1186/1471-2407-14-366 |
| SSID | ssj0000529419 |
| Score | 2.5425794 |
| Snippet | In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response... Abstract In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment... |
| SourceID | doaj swepub pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 4041 |
| SubjectTerms | 59/57 631/114/1564 631/67/2321 Diffusion coefficient Humanities and Social Sciences Magnetic resonance imaging multidisciplinary Noise Noise levels Science Science (multidisciplinary) |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LixQxEA6yIHgR346uEkFE0WbTSfqR47jrMgrrQVzZW8ircdDpGbanD1787VYlPc20ynrxOEmaSdcjVdWp-oqQ54ZbL4umzCpjfYYIcxkY4ZCJ4MA7F0KoWMd99rFcnMsPF8XFXqsvzAlL8MCJcEeVtIaFyjIPgYg3jTKubqSyZRkKUSiHpy_YvL1gKqF6cyVzNVTJMFEfdWCpsJoMDmWsSofAaWKJImD_37zMP5MlxxvT39BFo0U6vUVuDq4knadXuE2uhfYOuZ6aS_64S34uDH7EcN8oJnf0l4E2IaJ4dhRrSqjZYPJ5u6XYI6XHj2bUrUNElMDRl_OT41f07NN7ulzBmdPR1C6XwjIcgK1RAz8xi2STig1wCJHEV5hh090j56fvPh8vsqHbQubAJ9pmwDSeG4SYs4VnvmlyW3nrSs4ss14B8UwAbUeoB1WCE-IZhCaFccBqLx0L4j45aNdteEioEyZ3nlUNZ1KqhinuhMhD6R1zUlo3I_mO8toNUOTYEeO7jlfiotaJWxq4pSO3tJyR1-MzmwTEceXqt8jQcSWCaMcBEC09iJb-l2jNyOFOHPSg2Z3OEbKvrEtWz8izcRp0Ei9aTBvWPa6ByF9JWbAZeZCkZ9wJryHikzXMVBO5mmx1OtMuv0bcbwguc1nAu73ZSeDetq4gxYskpZN_OFl-mUdi9KteI0SlEo_-B80ekxs86liZcX5IDraXfXgCbtvWPo0a-gu8Dz8v priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lj9MwELZgERIXxJvCgoyEEAiidWLHiU-o7LIqSMsBsag3y68sFTQtTXPgwm9nxkkDAdRjbEdxPDP2zHjmG0Kemsx6kVcyKYz1CSLMJXAIh4QHB9o551zFPO6zD3J2Lt7P83nvcGv6sMrdnhg3ar9y6CM_ShEqTZaSla_X3xOsGoW3q30JjcvkCkKXYUhXMS8GHwveYolU9bkyjJdHDZxXmFMGWzPmpoP5NDqPImz__3TNf0Mmh3vTvzBG47l0eoNc7xVKOu044Ca5FOpb5GpXYvLHbfJzZtCV4b5SDPFoN4FWIWJ5NhQzS6hZYwh6vaVYKaVF1xl1qxBxJbD1-fTk-AU9-_iOLpaw8zS0K5pLYRg2wNSogUeMJVl3KQfYhHjiS4yzae6Q89O3n45nSV9zIXGgGW0TIF2WGgSas7lnvqpSW3jrZMYss17B4pkAMo-AD0qCKuIZGCi5cUBwLxwL_C45qFd1uE-o4yZ1nhVVxoRQFVOZ4zwN0jvmhLBuQtLdymvXA5JjXYxvOl6M81J31NJALR2ppcWEvBzeWXdwHHtHv0GCDiMRSjs2rDYXupdMXQhrWCgs82DpelMp48pKKCtlyHmuYJqHO3bQvXw3-jc3TsiToRskE69bTB1WLY4B-18JkbMJuddxzzCTrAS7T5TQU4z4ajTVcU-9-BLRv8HETEUO__Zqx4F_TGvPUjzruHT0hZPF52lcjHbZagSqVPzB_t99SK5lUXpkkmWH5GC7acMjUMu29nGUvV8NhzXk priority: 102 providerName: ProQuest – databaseName: SpringerOpen Free (Free internet resource, activated by CARLI) dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELZKERKXijdbCjISQiCIcGLHiY_LlmpBKgdEUW-WX4EVbHbVbA5c-O3MOA8RqCpxjO0kY8_YnrFnviHkmcmsF3klk8JYnyDCXAKbcEh4cKCdc85VjOM-_SiXZ-LDeX6-R7IhFiY67UdIy7hMD95hbxrYaDAYDNZUDCoHu-cauY7Q7SjVC7kYz1Xw5kqkqo-PYby85NXJHhSh-i_TL_91kxzvSv_CFY170cktctArkXTekX2b7IX6DrnRpZX8eZf8Who8vnDfKbp1QCdpFWJnG4rRJNRs0e283lHMjtLicRl1mxCxJLD0xfx48ZKefnpPV2tYbRraJcql0AwLgDRq4BH9R7ZdmAEWIYb4Gn1rmnvk7OTd58Uy6fMsJA60oV0C7MpSg-ByNvfMV1VqC2-dzJhl1isYPBNgniPIg5KgfngGRkluHDDZC8cCv0_2600dHhLquEmdZ0WVMSFUxVTmOE-D9I45IaybkXQYee16EHLMhfFDx8twXuqOWxq4pSO3tJiRV-M72w6C48rWb5GhY0uEz44Fm4uvuhcnXQhrWCgs82DdelMp48pKKCtlyHmugMyjQRx0P6cbnSJYnywlK2fk6VgNsxGvWEwdNi22AZtfCZGzGXnQSc9ISVaCrSdKqCkmcjUhdVpTr75FxG8wK1ORQ99eDxL4B1lXDMXzTkonfzhefZnHwWjXrUZwSsUP_--7j8jNLM4mmWTZEdnfXbThMahmO_skzsXfyzszig priority: 102 providerName: Springer Nature |
| Title | Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters |
| URI | https://link.springer.com/article/10.1038/s41598-017-04151-4 https://www.ncbi.nlm.nih.gov/pubmed/28642480 https://www.proquest.com/docview/1955968608 https://www.proquest.com/docview/1913394450 https://pubmed.ncbi.nlm.nih.gov/PMC5481454 https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-134993 https://doaj.org/article/74ba0e7b0d094daf9ac8f49b66e5359c |
| Volume | 7 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1ZixNBEG72QPBFvI2uoQURRUd7pnuuB5FsdpcYyCKrkX1r-hoNbiYxyYD74m-3qmcmbDTuU0hND6nU0VV91FeEPFeRtiIukiBV2gaIMBdAEHYBdwayc8557uu4R6fJYCyG5_H5DmnbHTUCXG5d2mE_qfHi4u2vn5cfwOHf1yXj2bslBCEsFIP5FgvOYU20S_YhMqXoqKMm3a-xvqNchHlTO7P9VUQHziApFwgUeSVUeUT_bWnov7cp10eqf8GP-pB1cpvcanJN2quN4w7ZceVdcqPuPnl5j_weKNzlMD8o3v6oFo4WzsN8LikWnVA1x9vp5YpiE5UKd9WomTkPOYHUl72j_is6OvtIJ1OYlJa07qdLYRgSgDWq4CteM5nX1QhIQqjxKV7BWd4n45PjL_1B0LRjCAwkTasAtBqFCjHodGyZLYpQp1abJGKaaZuDHJWD6QCxIPIEshTLYO0SKwO2YIVhjj8ge-WsdI8INVyFxrK0iJgQecHyyHAeusQaZoTQpkPCVvLSNFjl2DLjQvozc57JWnESFCe94qTokNfrd-Y1Use1ow9RoeuRiLLtCbPFN9k4rUyFVsylmllYBFtV5Mpkhch1kriYxzmwedCag2wtV4aI6ZdkCcs65Nn6MTgtnsSo0s0qHBNyrEiOWYc8rK1nzUlrfR2SbtjVBqubT8rJdw8MDqvPUMTw3960FniFrWtE8aK20o1fOJp87XlhVNNKIoZlzh__l9cn5GbkfSgJouiA7K0WlXsKydpKd8luep52yX6vN_w8hM_D49NPZ0DtJ_2u3wDpeh_9AxCVPqQ |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxELZKKgQXxJtAASMBAsGq3rX3dUAobVoltIlQ1aLejF8LEWQTslmhXvhJ_EZmdjcLAdRbj7GdZOwZz3jsmW8IeaoCbUWYRV6stPUQYc4DI-w87gyczjnnaZXHPRpHgxPx7jQ83SA_V7kwGFa50omVorYzg3fk2z5CpUVJxJK3828eVo3C19VVCY1aLA7c2Xdw2Yo3wz7w91kQ7O8d7w68pqqAZ8D2Lz0gLvAVQqnp0DKbZb6OrTZRwDTTNg3TRDmQaoQ0SCMwtpbBETxUBqZkhWGOw-9eIpuCgyvTIZs7e-P3R-2tDr6bCT9tsnMYT7YLsJCYxQbGALPhwWFbs4BVoYD_nW7_DdJsX2r_QjWtLOH-dXKtOcLSXi1zN8iGy2-Sy3VRy7Nb5MdA4eWJ-UIxqKRcOJq5Cj20oJjLQtUcg97zJcXaLCVe1lEzcxWSBba-6PV3X9LR0ZBOpqDrClqX6aUwDBuANKrgI0avzOskB2xCBPMpRvYUt8nJhfDjDunks9zdI9Rw5RvL4ixgQqQZSwPDue8ia5gRQpsu8VcrL00DgY6VOL7K6imeJ7LmlgRuyYpbUnTJq_Y78xoA5NzRO8jQdiSCd1cNs8Un2egCGQutmIs1s-BbW5WlyiSZSHUUuZCHKZC5tRIH2WiUQv6W_y550naDLsAHHpW7WYljfI6JziHrkru19LSUBAl4miKBnnhNrtZIXe_JJ58rvHFwan0RwtxeryTwD7LOWYrntZSu_UN_8qFXLUY5LSVCY6b8_vnTfUyuDI5Hh_JwOD54QK4G1U6KvCDYIp3lonQP4VC41I-anUjJx4ve_L8A1uJznw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELaWRSAuiDeFBYwECARRndh5HRAqW6qWZVcIsai34FeggqalaYT2wg_j1zHjPKCAettjbbcdex4e2zPfEPJABsqIMI-8WCrjIcKcB5uw9bjV4J1zzlOXx314FI2PxetpON0hP9tcGAyrbG2iM9RmofGOvO8jVFqURCzp501YxNvh6MXym4cVpPCltS2nUYvIgT35Dse38vlkCLx-GASjV-_3x15TYcDT4AesPSA08CXCqqnQMJPnvoqN0lHAFFMmDdNEWpBwhDdII9h4DQN3PJQapmeEZpbD754hZ2Me-qhj8TTu7nfwBU34aZOnw3jSL2GvxHw22BYwLx6Obht7oSsZ8D8_999wze7N9i98U7cnji6Ri40zSwe19F0mO7a4Qs7V5S1PrpIfY4nXKPoLxXWsVpbm1uGIlhSzWqhcYvh7saZYpaXCazuqF9ZhWmDr48Fw_wk9fDehszlYvZLWBXspDMMGII1K-IhxLMs63QGbEMt8jjE-5TVyfCrcuE52i0VhbxKqufS1YXEeMCHSnKWB5ty3kdFMC6F0j_jtyme6AUPHmhxfM_coz5Os5lYG3MoctzLRI0-77yxrKJCto18iQ7uRCOPtGharT1ljFbJYKMlsrJiBU7aReSp1kotURZENeZgCmXutOGSNbSmz35rQI_e7brAK-NQjC7uocIzPMeU5ZD1yo5aejpIggTOnSKAn3pCrDVI3e4rZZ4c8DsdbX4Qwt2etBP5B1paleFRL6cY_DGcfBm4xqnmVIUhmym9tn-49ch5UPnszOTq4TS4ETpEiLwj2yO56Vdk74B2u1V2nhpR8PG29_wX4inZv |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Haralick+texture+features+from+apparent+diffusion+coefficient+%28ADC%29+MRI+images+depend+on+imaging+and+pre-processing+parameters&rft.jtitle=Scientific+reports&rft.au=Brynolfsson%2C+Patrik&rft.au=Nilsson%2C+David&rft.au=Torheim%2C+Turid&rft.au=Asklund%2C+Thomas&rft.date=2017-06-22&rft.eissn=2045-2322&rft.volume=7&rft.issue=1&rft.spage=4041&rft_id=info:doi/10.1038%2Fs41598-017-04151-4&rft_id=info%3Apmid%2F28642480&rft.externalDocID=28642480 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |