Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23665
DC FieldValueLanguage
dc.contributor.authorLoizou, Christos P.-
dc.contributor.authorPantziaris, Marios-
dc.contributor.authorPattichis, Constantinos S.-
dc.contributor.authorSeimenis, Ioannis-
dc.date.accessioned2021-11-18T12:13:21Z-
dc.date.available2021-11-18T12:13:21Z-
dc.date.issued2013-02-
dc.identifier.citationJournal of Biomedical Graphics and Computing, 2013, vol. 3, no. 1, pp. 20-34en_US
dc.identifier.issn1925-4016-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/23665-
dc.description.abstractA problem that occurs in quantitative texture analysis of magnetic resonance imaging (MRI) is that there are intra-scan and inter-scan image intensity variations due to the MRI instrumentation. Therefore, image intensity normalization methods should be applied prior to further image analysis. The objective of this work was to investigate six previously described MRI intensity normalization methods and propose the most appropriate for the pre-processing of brain T2-weighted MR images acquired from 38 symptomatic untreated multiple sclerosis (MS) subjects. The following normalization methods were investigated: Contrast Stretch Normalization (CSN), Intensity Scaling (IS), Histogram Stretching (HS), Histogram Normalization (HN), Gaussian Kernel Normalization (GKN), and Histogram Equalization (HE). The main findings of this study can be summarized as follows: 1) Lesion texture features were affected differently by the normalization process for both the 0 and 6-12 months MRI scans. For example, for the features median and contrast there was significant difference between 0 and 6-12 months for the original MRI images but not for the HN normalized ones. On the other hand for the feature complexity there was no significant difference between 0 and 6-12 months for the original MRI images, but there was for the HN normalized images. 2) The statistical lesion feature analysis between the original and the normalized images showed that the HN method gave the highest number of significant features after normalization for both the 0 and 6-12 months MRI scans. 3) The HN normalization method gave the best performance compared to the other normalization methods with respect to the distance measures, structural similarity index, coefficient of variation, and correlation coefficient between the original and the normalized 0 and 6-12 months MRI scans. Thus, based on the above findings it is recommended that the simple HN normalization method could be used prior to quantitative texture analysis in the case study presented. The findings of this study provide evidence that texture features of MRI-detectable brain white matter lesions may have an additional potential role in the clinical evaluation of MRI images in MS. This is a prerequisite step in the assessment of texture features as surrogate markers of disease progression. However, a larger scale study is needed to establish the application in clinical practice and for computing shape parameters and texture features that may provide information for better and earlier differentiation between normal tissue and MS lesions.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Biomedical Graphics and Computingen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMagnetic resonance imagingen_US
dc.subjectMultiple sclerosisen_US
dc.subjectTexture analysisen_US
dc.subjectImage normalizationen_US
dc.titleBrain MR image normalization in texture analysis of multiple sclerosisen_US
dc.typeArticleen_US
dc.collaborationIntercollegeen_US
dc.collaborationCyprus Institute of Neurology and Geneticsen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationAyios Therissos Medical Diagnostic Centeren_US
dc.subject.categoryMedical Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.5430/jbgc.v3n1p20en_US
dc.relation.issue1en_US
dc.relation.volume3en_US
cut.common.academicyear2012-2013en_US
dc.identifier.spage20en_US
dc.identifier.epage34en_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.grantfulltextopen-
crisitem.journal.journalissn1925-4016-
crisitem.journal.publisherSciedu Press-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-1247-8573-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Άρθρα/Articles
Files in This Item:
File Description SizeFormat
929-6503-1-PB.pdf531.22 kBAdobe PDFView/Open
CORE Recommender
Show simple item record

Page view(s) 20

189
Last Week
0
Last month
1
checked on Feb 29, 2024

Download(s) 20

186
checked on Feb 29, 2024

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons