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
Density Estimation of Grey-Level Co-Occurrence Matrices for Image Texture Analysis
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
Show others and affiliations
2018 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 63, no 19, p. 9-15, article id 195017Article in journal (Refereed) Published
Abstract [en]

The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI).

The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features.

Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes.

The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about 20×20).

In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.

Place, publisher, year, edition, pages
Institute of Physics and Engineering in Medicine, 2018. Vol. 63, no 19, p. 9-15, article id 195017
Keywords [en]
Haralick features, invariant features, GLCM, density estimation, texture analysis, image analysis
National Category
Computer Vision and Robotics (Autonomous Systems) Other Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-152488DOI: 10.1088/1361-6560/aad8ecISI: 000446205200005PubMedID: 30088815OAI: oai:DiVA.org:umu-152488DiVA, id: diva2:1254015
Funder
Västerbotten County CouncilVINNOVAAvailable from: 2018-10-08 Created: 2018-10-08 Last updated: 2018-10-31Bibliographically approved

Open Access in DiVA

fulltext(2604 kB)63 downloads
File information
File name FULLTEXT01.pdfFile size 2604 kBChecksum SHA-512
8be51c71ea339c31d50ec53822d5a42fb81370a9223701057ec47b321914b851f453ce751261889192aff6109041f3f51f7e080942ec46d3ac207d641e42fb36
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Authority records BETA

Garpebring, AndersBrynolfsson, PatrikNyholm, TufveLöfstedt, Tommy

Search in DiVA

By author/editor
Garpebring, AndersBrynolfsson, PatrikNyholm, TufveLöfstedt, Tommy
By organisation
Department of Radiation Sciences
In the same journal
Physics in Medicine and Biology
Computer Vision and Robotics (Autonomous Systems)Other Mathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 63 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: 214 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