umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Gray-level invariant Haralick texture features
Umeå University, Faculty of Medicine, Department of Radiation Sciences.ORCID iD: 0000-0001-7119-7646
Umeå University, Faculty of Medicine, Department of Radiation Sciences.ORCID iD: 0000-0002-0455-8904
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
2019 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 2, article id e0212110Article in journal (Refereed) Published
Abstract [en]

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.

Place, publisher, year, edition, pages
Public Library of Science , 2019. Vol. 14, no 2, article id e0212110
National Category
Medical Biotechnology
Identifiers
URN: urn:nbn:se:umu:diva-134995DOI: 10.1371/journal.pone.0212110ISI: 000459709100037PubMedID: 30794577Scopus ID: 2-s2.0-85062005861OAI: oai:DiVA.org:umu-134995DiVA, id: diva2:1095684
Note

Originally included in thesis in manuscript form

Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2019-11-19Bibliographically approved
In thesis
1. Applications of statistical methods in quantitative magnetic resonance imaging
Open this publication in new window or tab >>Applications of statistical methods in quantitative magnetic resonance imaging
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI).

DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners.

DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start.

However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways.

In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2017. p. 65
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1900
Keywords
Quantitative imaging, tumor imaging, dynamic contrast-enhanced MRI, diffusion weighted MRI, texture analysis
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
radiation physics
Identifiers
urn:nbn:se:umu:diva-134997 (URN)978-91-7601-729-6 (ISBN)
Public defence
2017-06-09, Bergasalen, byggnad 27, Norrlands universitetssjukhus, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2017-05-19 Created: 2017-05-15 Last updated: 2018-06-09Bibliographically approved

Open Access in DiVA

fulltext(3294 kB)111 downloads
File information
File name FULLTEXT01.pdfFile size 3294 kBChecksum SHA-512
f9c5617e2d6d312bb02e54a6ffed0ec33f525137fedcd040df8f2538cb0bd0d849ce6631a02f52d9e3e3f1bc4d6bab3ec1255542e55191092b0bfb2b97675535
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records BETA

Löfstedt, TommyBrynolfsson, PatrikNyholm, TufveGarpebring, Anders

Search in DiVA

By author/editor
Löfstedt, TommyBrynolfsson, PatrikNyholm, TufveGarpebring, Anders
By organisation
Department of Radiation Sciences
In the same journal
PLoS ONE
Medical Biotechnology

Search outside of DiVA

GoogleGoogle Scholar
Total: 111 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 500 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf