Characterizing healthy and osteoarthritic knee cartilage on phase contrast CT with geometric texture features

Mahesh B. Nagarajan; Paola Coan; Markus B. Huber; Paul C. Diemoz; Christian Glaser; Axel Wismüller

doi:10.1117/12.2006255

29 March 2013 Characterizing healthy and osteoarthritic knee cartilage on phase contrast CT with geometric texture features

Mahesh B. Nagarajan, Paola Coan, Markus B. Huber, Paul C. Diemoz, Christian Glaser, Axel Wismüller

Author Affiliations +

Proceedings Volume 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging; 86721J (2013) https://doi.org/10.1117/12.2006255
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, 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 imaging (PCI) with computed tomography (CT) was shown to allow direct examination of chondrocyte 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 texture analysis. Statistical features derived from gray-level co-occurrence matrices (GLCM) and geometric features derived from the Scaling Index Method (SIM) 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 Receiver Operating Characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM and GLCM correlation features. With the experimental conditions used in this study, both SIM and GLCM achieved a high classification performance (AUC value of 0.98) in the task of distinguishing between healthy 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, Paul C. Diemoz, Christian Glaser, and Axel Wismüller "Characterizing healthy and osteoarthritic knee cartilage on phase contrast CT with geometric texture features", Proc. SPIE 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, 86721J (29 March 2013); https://doi.org/10.1117/12.2006255

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KEYWORDS

Cartilage

Feature extraction

Statistical analysis

Computed tomography

Machine learning

Fuzzy logic

Phase contrast

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