Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT

Michael Howes, Mariusz Bajger, Gobert Lee, Francesca Bucci, Saulo Martelli

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Statistical Region Merging technique belongs to the portfolio of very successful image segmentation methods across diverse domains and applications. The method is based on a solid probabilistic principle and was extended in various directions to suit specific applications, including those from medical domains. In its basic implementation the technique is based on a merging criterion relying on image pixel intensities. Sufficient to segment well some natural scene images, it often deteriorates dramatically when challenging medical images are segmented. In this study we introduce a new merging criterion into the method which utilizes texture characteristic of the image. We demonstrate that the enhanced criterion allows segmentation of knee bones in CT comparable to state-of-the-art outcomes found in literature while preserving the desirable properties of the original technique.

Original languageEnglish
Title of host publication 2021 Digital Image Computing
Subtitle of host publicationTechniques and Applications DICTA
EditorsJun Zhou, Olivier Salvado, Ferdous Sohel, Paulo Borges, Shilin Wang
Place of PublicationNew York, USA
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781665417099, 9781665417082
DOIs
Publication statusPublished - 2021
Event2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021 - Gold Coast, Australia
Duration: 29 Nov 20211 Dec 2021

Conference

Conference2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021
Country/TerritoryAustralia
CityGold Coast
Period29/11/211/12/21

Keywords

  • CT
  • Knee segmentation
  • Statistical region merging
  • Superpixels
  • Texture classification

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