Characterization of healthy and osteoarthritic chondrocyte cell patterns on phase contrast CT images of the knee cartilage matrix

Mahesh B. Nagarajan; Paola Coan; Markus B. Huber; Chien-Chun Yang; Christian Glaser; Maximilian F. Reiser; Axel Wismüller

doi:10.1117/12.910919

16 April 2012 Characterization of healthy and osteoarthritic chondrocyte cell patterns on phase contrast CT images of the knee cartilage matrix

Mahesh B. Nagarajan, Paola Coan, Markus B. Huber, Chien-Chun Yang, Christian Glaser, Maximilian F. Reiser, Axel Wismüller

Author Affiliations +

Proceedings Volume 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging; 831720 (2012) https://doi.org/10.1117/12.910919
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

The current approach to evaluating cartilage degeneration at the knee joint requires visualization of the joint space on radiographic images where indirect cues such as joint space narrowing serve as markers for osteoarthritis. A recent novel approach to visualizing the knee cartilage matrix using phase contrast CT imaging (PCI-CT) was shown to allow direct examination of chondrocyte cell patterns and their subsequent correlation to osteoarthritis. This study aims to characterize chondrocyte cell patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage through both gray-level co-occurrence matrix (GLCM) derived texture features as well as Minkowski Functionals (MF). Thirteen GLCM and three MF texture features were extracted from 404 regions of interest (ROI) annotated on PCI images of healthy and osteoarthritic specimens of knee cartilage. These texture features were then used in a machine learning task to classify ROIs as healthy or osteoarthritic. A fuzzy k-nearest neighbor classifier was used and its performance was evaluated using the area under the ROC curve (AUC). The best classification performance was observed with the MF features 'perimeter' and 'Euler characteristic' and with GLCM correlation features (f3 and f13). With the experimental conditions used in this study, both Minkowski Functionals and GLCM achieved a high classification performance (AUC value of 0.97) in the task of distinguishing between health and osteoarthritic ROIs. These results show that such quantitative analysis of chondrocyte patterns in the knee cartilage matrix can distinguish between healthy and osteoarthritic tissue with high accuracy.

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Mahesh B. Nagarajan, Paola Coan, Markus B. Huber, Chien-Chun Yang, Christian Glaser, Maximilian F. Reiser, and Axel Wismüller "Characterization of healthy and osteoarthritic chondrocyte cell patterns on phase contrast CT images of the knee cartilage matrix", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 831720 (16 April 2012); https://doi.org/10.1117/12.910919

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KEYWORDS

Cartilage

Feature extraction

Computed tomography

Machine learning

Fuzzy logic

Statistical analysis

Visualization

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