Document Detail

Automated classification of articular cartilage surfaces based on surface texture.
MedLine Citation:
PMID:  17236517     Owner:  NLM     Status:  MEDLINE    
In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.
G P Stachowiak; G W Stachowiak; P Podsiadlo
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Publication Detail:
Type:  Evaluation Studies; In Vitro; Journal Article    
Journal Detail:
Title:  Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine     Volume:  220     ISSN:  0954-4119     ISO Abbreviation:  Proc Inst Mech Eng H     Publication Date:  2006 Nov 
Date Detail:
Created Date:  2007-01-22     Completed Date:  2007-02-22     Revised Date:  2009-06-08    
Medline Journal Info:
Nlm Unique ID:  8908934     Medline TA:  Proc Inst Mech Eng H     Country:  England    
Other Details:
Languages:  eng     Pagination:  831-43     Citation Subset:  IM    
Tribology Laboratory, Department of Mechanical Engineering, University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia.
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MeSH Terms
Artificial Intelligence*
Cartilage, Articular / ultrastructure*
Image Interpretation, Computer-Assisted / methods*
Information Storage and Retrieval / methods
Microscopy, Electron, Scanning / methods*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Surface Properties

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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