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

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

doi:10.1109/DICTA52665.2021.9647224

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 proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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 language	English
Title of host publication	2021 Digital Image Computing
Subtitle of host publication	Techniques and Applications DICTA
Editors	Jun Zhou, Olivier Salvado, Ferdous Sohel, Paulo Borges, Shilin Wang
Place of Publication	New York, USA
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	8
ISBN (Electronic)	9781665417099, 9781665417082
DOIs	https://doi.org/10.1109/DICTA52665.2021.9647224
Publication status	Published - 2021
Event	2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021 - Gold Coast, Australia Duration: 29 Nov 2021 → 1 Dec 2021

Conference

Conference	2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021
Country/Territory	Australia
City	Gold Coast
Period	29/11/21 → 1/12/21

Keywords

CT
Knee segmentation
Statistical region merging
Superpixels
Texture classification

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10.1109/DICTA52665.2021.9647224Licence: In Copyright

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Howes, M., Bajger, M., Lee, G., Bucci, F., & Martelli, S. (2021). Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT. In J. Zhou, O. Salvado, F. Sohel, P. Borges, & S. Wang (Eds.), 2021 Digital Image Computing : Techniques and Applications DICTA Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA52665.2021.9647224

Howes, Michael ; Bajger, Mariusz ; Lee, Gobert et al. / Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT. 2021 Digital Image Computing : Techniques and Applications DICTA. editor / Jun Zhou ; Olivier Salvado ; Ferdous Sohel ; Paulo Borges ; Shilin Wang. New York, USA : Institute of Electrical and Electronics Engineers, 2021.

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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.",

keywords = "CT, Knee segmentation, Statistical region merging, Superpixels, Texture classification",

author = "Michael Howes and Mariusz Bajger and Gobert Lee and Francesca Bucci and Saulo Martelli",

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language = "English",

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booktitle = "2021 Digital Image Computing",

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note = "2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021 ; Conference date: 29-11-2021 Through 01-12-2021",

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Howes, M, Bajger, M , Lee, G , Bucci, F & Martelli, S 2021, Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT. in J Zhou, O Salvado, F Sohel, P Borges & S Wang (eds), 2021 Digital Image Computing : Techniques and Applications DICTA. Institute of Electrical and Electronics Engineers, New York, USA, 2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021, Gold Coast, Australia, 29/11/21. https://doi.org/10.1109/DICTA52665.2021.9647224

Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT. / Howes, Michael; Bajger, Mariusz ; Lee, Gobert et al.
2021 Digital Image Computing : Techniques and Applications DICTA. ed. / Jun Zhou; Olivier Salvado; Ferdous Sohel; Paulo Borges; Shilin Wang. New York, USA: Institute of Electrical and Electronics Engineers, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT

AU - Howes, Michael

AU - Bajger, Mariusz

AU - Lee, Gobert

AU - Bucci, Francesca

AU - Martelli, Saulo

PY - 2021

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N2 - 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.

AB - 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.

KW - CT

KW - Knee segmentation

KW - Statistical region merging

KW - Superpixels

KW - Texture classification

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A2 - Zhou, Jun

A2 - Salvado, Olivier

A2 - Sohel, Ferdous

A2 - Borges, Paulo

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ER -

Howes M, Bajger M , Lee G , Bucci F, Martelli S. Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT. In Zhou J, Salvado O, Sohel F, Borges P, Wang S, editors, 2021 Digital Image Computing : Techniques and Applications DICTA. New York, USA: Institute of Electrical and Electronics Engineers. 2021 doi: 10.1109/DICTA52665.2021.9647224

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

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