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Granulometry-based trabecular bone segmentation
KTH, School of Technology and Health (STH).
KTH, School of Technology and Health (STH). Linköping University, Sweden.
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.ORCID iD: 0000-0002-7750-1917
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2017 (English)In: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, 100-108 p.Conference paper, (Refereed)
Abstract [en]

The accuracy of the analyses for studying the three dimensional trabecular bone microstructure rely on the quality of the segmentation between trabecular bone and bone marrow. Such segmentation is challenging for images from computed tomography modalities that can be used in vivo due to their low contrast and resolution. For this purpose, we propose in this paper a granulometry-based segmentation method. In a first step, the trabecular thickness is estimated by using the granulometry in gray scale, which is generated by applying the opening morphological operation with ball-shaped structuring elements of different diameters. This process mimics the traditional sphere-fitting method used for estimating trabecular thickness in segmented images. The residual obtained after computing the granulometry is compared to the original gray scale value in order to obtain a measurement of how likely a voxel belongs to trabecular bone. A threshold is applied to obtain the final segmentation. Six histomorphometric parameters were computed on 14 segmented bone specimens imaged with cone-beam computed tomography (CBCT), considering micro-computed tomography (micro-CT) as the ground truth. Otsu’s thresholding and Automated Region Growing (ARG) segmentation methods were used for comparison. For three parameters (Tb.N, Tb.Th and BV/TV), the proposed segmentation algorithm yielded the highest correlations with micro-CT, while for the remaining three (Tb.Nd, Tb.Tm and Tb.Sp), its performance was comparable to ARG. The method also yielded the strongest average correlation (0.89). When Tb.Th was computed directly from the gray scale images, the correlation was superior to the binary-based methods. The results suggest that the proposed algorithm can be used for studying trabecular bone in vivo through CBCT.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10270, 100-108 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]
Cone beam computed tomography, Granulometry, Segmentation, Trabecular bone
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-210015DOI: 10.1007/978-3-319-59129-2_9Scopus ID: 2-s2.0-85020456098ISBN: 9783319591285 (print)OAI: oai:DiVA.org:kth-210015DiVA: diva2:1115960
Conference
20th Scandinavian Conference on Image Analysis, SCIA 2017, Tromso, Norway, 12 June 2017 through 14 June 2017
Note

QC 20170627

Available from: 2017-06-27 Created: 2017-06-27 Last updated: 2017-06-27Bibliographically approved

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Chowdhury, ManishKlintström, BenjaminSmedby, ÖrjanMoreno, Rodrigo
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Citation style
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