Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features

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

doi:10.1117/12.2042395

13 March 2014 Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features

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

Author Affiliations +

Proceedings Volume 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging; 903811 (2014) https://doi.org/10.1117/12.2042395
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Current assessment of cartilage is primarily based on identification of indirect markers such as joint space narrowing and increased subchondral bone density on x-ray images. In this context, phase contrast CT imaging (PCI-CT) has recently emerged as a novel imaging technique that allows a direct examination of chondrocyte patterns and their correlation to osteoarthritis through visualization of cartilage soft tissue. This study investigates the use of topological and geometrical approaches for characterizing chondrocyte patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage. For this purpose, topological features derived from Minkowski Functionals and geometric features derived from the Scaling Index Method (SIM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of healthy and osteoarthritic specimens of human patellar cartilage. The extracted features were then used in a machine learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification 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 (0.95 ± 0.06) which outperformed all Minkowski Functionals (p < 0.001). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving SIM-derived geometrical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

Citation Download Citation

Mahesh B. Nagarajan, Paola Coan, Markus B. Huber, Paul C. Diemoz, and Axel Wismüller "Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 903811 (13 March 2014); https://doi.org/10.1117/12.2042395

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