Abstract
Segmentation of scanned tissue volumes of three-dimensional (3D) images often involves - at least partially - some manual process, as there is no standardized automatic method. A well-performing automatic segmentation would be preferable, not only because it would improve segmentation speed, but also because it would be user-independent and provide more objectivity to the task. Here we extend a 3D local binary patterns (LBP) based trabecular bone segmentation method with adaptive local thresholding and additional segmentation parameters to make it more robust yet still perform adequately when compared to traditional user-assisted segmentation. We estimate parameters for the new segmentation method (AMLM) in our experimental setting, and have two micro-computed tomography (µCT) scanned bovine trabecular bone tissue volumes segmented by both the AMLM and two experienced users. Comparison of the results shows superior performance of the AMLM.
| Original language | English |
|---|---|
| Title of host publication | Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings |
| Editors | Puneet Sharma, Filippo Maria Bianchi |
| Publisher | Springer |
| Pages | 221-232 |
| Number of pages | 12 |
| ISBN (Electronic) | 978-3-319-59129-2 |
| ISBN (Print) | 978-3-319-59128-5 |
| DOIs | |
| Publication status | Published - 1 Jun 2017 |
| MoE publication type | A4 Article in a conference publication |
| Event | 20th Scandinavian Conference on Image Analysis, SCIA 2017 - Tromso, Norway Duration: 12 Jun 2017 → 14 Jun 2017 |
Publication series
| Series | Lecture Notes in Computer Science |
|---|---|
| Volume | 10270 |
| ISSN | 0302-9743 |
Conference
| Conference | 20th Scandinavian Conference on Image Analysis, SCIA 2017 |
|---|---|
| Country | Norway |
| City | Tromso |
| Period | 12/06/17 → 14/06/17 |
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Keywords
- 3D
- LBP
- Micro-CT
- Segmentation
Cite this
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Automatic segmentation of bone tissue from computed tomography using a volumetric local binary patterns based method. / Kaipala, Jukka; López, Miguel Bordallo; Saarakkala, Simo; Thevenot, Jérôme.
Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings. ed. / Puneet Sharma; Filippo Maria Bianchi. Springer, 2017. p. 221-232 (Lecture Notes in Computer Science, Vol. 10270).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Automatic segmentation of bone tissue from computed tomography using a volumetric local binary patterns based method
AU - Kaipala, Jukka
AU - López, Miguel Bordallo
AU - Saarakkala, Simo
AU - Thevenot, Jérôme
PY - 2017/6/1
Y1 - 2017/6/1
N2 - Segmentation of scanned tissue volumes of three-dimensional (3D) images often involves - at least partially - some manual process, as there is no standardized automatic method. A well-performing automatic segmentation would be preferable, not only because it would improve segmentation speed, but also because it would be user-independent and provide more objectivity to the task. Here we extend a 3D local binary patterns (LBP) based trabecular bone segmentation method with adaptive local thresholding and additional segmentation parameters to make it more robust yet still perform adequately when compared to traditional user-assisted segmentation. We estimate parameters for the new segmentation method (AMLM) in our experimental setting, and have two micro-computed tomography (µCT) scanned bovine trabecular bone tissue volumes segmented by both the AMLM and two experienced users. Comparison of the results shows superior performance of the AMLM.
AB - Segmentation of scanned tissue volumes of three-dimensional (3D) images often involves - at least partially - some manual process, as there is no standardized automatic method. A well-performing automatic segmentation would be preferable, not only because it would improve segmentation speed, but also because it would be user-independent and provide more objectivity to the task. Here we extend a 3D local binary patterns (LBP) based trabecular bone segmentation method with adaptive local thresholding and additional segmentation parameters to make it more robust yet still perform adequately when compared to traditional user-assisted segmentation. We estimate parameters for the new segmentation method (AMLM) in our experimental setting, and have two micro-computed tomography (µCT) scanned bovine trabecular bone tissue volumes segmented by both the AMLM and two experienced users. Comparison of the results shows superior performance of the AMLM.
KW - 3D
KW - LBP
KW - Micro-CT
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85020480785&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59129-2_19
DO - 10.1007/978-3-319-59129-2_19
M3 - Conference article in proceedings
AN - SCOPUS:85020480785
SN - 978-3-319-59128-5
T3 - Lecture Notes in Computer Science
SP - 221
EP - 232
BT - Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings
A2 - Sharma, Puneet
A2 - Bianchi, Filippo Maria
PB - Springer
ER -