Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage
A close relationship has been found between the 3D collagen structure and physiological condition of articular cartilage (AC). Studying the 3D collagen network in AC offers a way to determine the condition of the cartilage. However, traditional qualitative studies are time consuming and subjective....
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/34480 |
| _version_ | 1848754234341720064 |
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| author | Duan, X. Wu, Ping Swift, B. Kirk, Brett |
| author_facet | Duan, X. Wu, Ping Swift, B. Kirk, Brett |
| author_sort | Duan, X. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A close relationship has been found between the 3D collagen structure and physiological condition of articular cartilage (AC). Studying the 3D collagen network in AC offers a way to determine the condition of the cartilage. However, traditional qualitative studies are time consuming and subjective. This study aims to develop a computer vision-based classifier to automatically determine the condition of AC tissue based on the structural characteristics of the collagen network. Textureanalysis was applied to quantitatively characterise the 3D collagen structure in normal (International Cartilage RepairSociety, ICRS, grade 0), aged (ICRS grade 1) and osteoarthritic cartilages (ICRS grade 2). Principle component techniques and linear discriminant analysis were then used to classify the microstructural characteristics of the 3D collagen meshwork and the condition of the AC. The 3D collagen meshwork in the three physiological condition groups displayed distinctive characteristics. Texture analysis indicated a significant difference in the mean texture parameters of the 3D collagen network between groups. The principle component and linear discriminant analysis of the texture data allowed for the development of a classifier for identifying the physiological status of the AC with an expected prediction error of 4.23%. An automatic image analysis classifier has been developed to predict the physiological condition of AC (from ICRS grade 0 to 2) based on texture data from the 3D collagen network in the tissue. |
| first_indexed | 2025-11-14T08:37:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-34480 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:37:10Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-344802017-09-13T15:10:43Z Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage Duan, X. Wu, Ping Swift, B. Kirk, Brett automatic physical classification system articular cartilage osteoarthritis collagen structure texture analysis A close relationship has been found between the 3D collagen structure and physiological condition of articular cartilage (AC). Studying the 3D collagen network in AC offers a way to determine the condition of the cartilage. However, traditional qualitative studies are time consuming and subjective. This study aims to develop a computer vision-based classifier to automatically determine the condition of AC tissue based on the structural characteristics of the collagen network. Textureanalysis was applied to quantitatively characterise the 3D collagen structure in normal (International Cartilage RepairSociety, ICRS, grade 0), aged (ICRS grade 1) and osteoarthritic cartilages (ICRS grade 2). Principle component techniques and linear discriminant analysis were then used to classify the microstructural characteristics of the 3D collagen meshwork and the condition of the AC. The 3D collagen meshwork in the three physiological condition groups displayed distinctive characteristics. Texture analysis indicated a significant difference in the mean texture parameters of the 3D collagen network between groups. The principle component and linear discriminant analysis of the texture data allowed for the development of a classifier for identifying the physiological status of the AC with an expected prediction error of 4.23%. An automatic image analysis classifier has been developed to predict the physiological condition of AC (from ICRS grade 0 to 2) based on texture data from the 3D collagen network in the tissue. 2014 Journal Article http://hdl.handle.net/20.500.11937/34480 10.1080/10255842.2013.864284 Elsevier restricted |
| spellingShingle | automatic physical classification system articular cartilage osteoarthritis collagen structure texture analysis Duan, X. Wu, Ping Swift, B. Kirk, Brett Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage |
| title | Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage |
| title_full | Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage |
| title_fullStr | Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage |
| title_full_unstemmed | Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage |
| title_short | Texture analysis of the 3D collagen network and automatic classification of the physiology of articular cartilage |
| title_sort | texture analysis of the 3d collagen network and automatic classification of the physiology of articular cartilage |
| topic | automatic physical classification system articular cartilage osteoarthritis collagen structure texture analysis |
| url | http://hdl.handle.net/20.500.11937/34480 |