Automatic Human Knee Cartilage Segmentation from Multi-contrast MR Images Using Extreme Learning Machines and Discriminative Random Fields

Abstract

Accurate and automatic segmentation of knee cartilage is required for the quantitative cartilage measures and is crucial for the assessment of acute injury or osteoarthritis. Unfortunately, the current works are still unsatisfactory. In this paper, we present a novel solution toward the automatic cartilage seg-mentation from multi-contrast magnetic resonance (MR) images using a pixel classification approach. Most of the previous classification based works for cartilage segmentation only rely on the labeling by a trained classifier, such as support vector machines (SVM) or k-nearest neighbor, but they do not consider the spatial interaction. Extreme learning machines (ELM) have been proposed as the training algorithm for the generalized single-hidden layer feedforward networks, which can be used in various regression and classification applica-tions. Works on ELM have shown that ELM for classification not only tends to achieve good generalization performance, but also is easy to be implemented since ELM requires less human intervention (only one user-specified parameter needs to be chosen) and can get direct least-square solution. To incorporate spatial dependency in classification, we propose a new segmentation method based on the convex optimization of an ELM-based association potential and a discriminative random fields (DRF) based interaction potential for segmenting cartilage automatically with multi-contrast MR images. Our method not only benefits from the good generalization classification performance of ELM but also incorporates the spatial dependencies in classification. We test the pro-posed method on multi-contrast MR datasets acquired from 11 subjects. Experimental results show that our method outperforms the classifiers based solely on DRF, SVM or ELM in segmentation accuracy.