Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling

摘要

Anatomical structures that can deteriorate over time, such as cartilage, can be successfully delineated with voxel-classification approaches in magnetic resonance (MR) images. However, segmentation via voxel-classification is a computationally demanding process for high-field MR images with high spatial resolutions. In this study, the whole femoral, tibial, and patellar cartilage compartments in the knee joint were automatically segmented in high-field MR images obtained from Osteoarthritis Initiative using a voxel-classification-driven region-growing algorithm with sample-expand method. Computational complexity of the classification was alleviated via subsampling of the background voxels in the training MR images and selecting a small subset of significant features by taking into consideration systems with limited memory and processing power. Although subsampling of the voxels may lead to a loss of generality of the training models and a decrease in segmentation accuracies, effective subsampling strategies can overcome these problems. Therefore, different subsampling techniques, which involve uniform, Gaussian, vicinity-correlated (VC) sparse, and VC dense subsampling, were used to generate four training models. The segmentation system was experimented using 10 training and 23 testing MR images, and the effects of different training models on segmentation accuracies were investigated. Experimental results showed that the highest mean Dice similarity coefficient (DSC) values for all compartments were obtained when the training models of VC sparse subsampling technique were used. Mean DSC values optimized with this technique were 82.6%, 83.1%, and 72.6% for femoral, tibial, and patellar cartilage compartments, respectively, when mean sensitivities were 79.9%, 84.0%, and 71.5%, and mean specificities were 99.8%, 99.9%, and 99.9%. (C) 2016 Elsevier Ltd. All rights reserved.