Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping

• Published in: Bio-medical materials and engineering Vol. 24; no. 6; pp. 3379 - 3388
• Main Authors: Wu, Yixiao, Yang, Ran, Jia, Sen, Li, Zhanjun, Zhou, Zhiyang, Lou, Ting
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2014
• Subjects: Accuracy; Algorithms; Biocompatibility; Biomedical engineering; Cartilage, Articular - pathology; Classifiers; Diagnosis; Early Diagnosis; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Knees; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical models; Neural Networks (Computer); Osteoarthritis, Knee - pathology; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Knee OA; MR T2 mapping; RBF neural network; computer-aided diagnosis
• ISSN: 0959-2989; 1878-3619; 1878-3619
• DOI: 10.3233/BME-141161

This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.