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Feature space clustering for trabecular bone segmentation
KTH, School of Technology and Health (STH).
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-7750-1917
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0001-5765-2964
2017 (English)In: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, 65-75 p.Conference paper, (Refereed)
Abstract [en]

Trabecular bone structure has been shown to impact bone strength and fracture risk. In vitro, this structure can be measured by micro-computed tomography (micro-CT). For clinical use, it would be valuable if multi-slice computed tomography (MSCT) could be used to analyse trabecular bone structure. One important step in the analysis is image volume segmentation. Previous segmentation techniques have either been computer resource intensive or produced sub-optimal results when used on MSCT data. This paper proposes a new segmentation method that tries to balance good results against computational complexity. Material. Fourteen human radius specimens where scanned with MSCT and segmented using the proposed method as well as two segmentation methods previously used to segment trabecular bone (Otsu and Automated Region Growing (ARG)). The proposed method (named FCH) uses a combination of feature space clustering, edge detection and hysteresis thresholding. For evaluation, we computed correlations with the reference method micro-CT for 7 structure parameters and measured segmentation time. Results. Correlations with micro-CT were highest for FCH in 3 cases, highest for ARG in 3 cases, and in general lower for Otsu. Both FCH and ARG had correlations higher than 0.80 for all parameters, except for trabecular thickness and trabecular termini. FCH was 60 times slower than Otsu, but 5 times faster than ARG. Discussion. The high correlations with micro-CT suggest that with a suitable segmentation method it might be possible to analyse trabecular bone structure using MSCT-machines. The proposed segmentation method may represent a useful balance between speed and accuracy.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10270, 65-75 p.
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10270
Keyword [en]
Clustering, Feature-space, Segmentation, Trabecular bone
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-210008DOI: 10.1007/978-3-319-59129-2_6Scopus ID: 2-s2.0-85020413922ISBN: 9783319591285 (print)OAI: oai:DiVA.org:kth-210008DiVA: diva2:1116867
Conference
20th Scandinavian Conference on Image Analysis, SCIA 2017, Tromso, Norway, 12 June 2017 through 14 June 2017
Note

QC 20170628

Available from: 2017-06-28 Created: 2017-06-28 Last updated: 2017-06-28Bibliographically approved

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Klintström, BenjaminSmedby, ÖrjanMoreno, Rodrigo
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CiteExportLink to record
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Citation style
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