Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation.

In this paper, we propose a dense multi-scale adaptive graph convolutional network () method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously.

A novel multi-atlas segmentation approach under the semi-supervised learning framework: Application to knee cartilage segmentation.

Background And Objective: Multi-atlas based segmentation techniques, which rely on an atlas library comprised of training images labeled by an expert, have proven their effectiveness in multiple automatic segmentation applications. However, the usage of exhaustive patch libraries combined with the voxel-wise labeling incur a large computational cost in terms of memory requirements and execution times.

Methods: To confront this shortcoming, we propose a novel two-stage multi-atlas approach designed under the Semi-Supervised Learning (SSL) framework.

Feature selection based on a fuzzy complementary criterion: application to gait recognition using ground reaction forces.

An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject recognition using ground reaction force measurements. Our approach relies on a local fuzzy evaluation measure with respect to patterns that reveal the adequacy of data coverage for each feature. Furthermore, FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that those features are iteratively introduced, providing the maximum additional contribution with regard to the information content given by the previously selected features.

A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements.

A novel fuzzy decision tree-based SVM (FDT-SVM) classifier is proposed in this paper, to distinguish between asymptotic (AS) and osteoarthritis (OA) knee gait patterns and to investigate OA severity using 3-D ground reaction force (GRF) measurements. FDT-SVM incorporates effective techniques for feature selection (FS) and class grouping (CG) at each non-leaf nodes of the tree structure, which reduce the overall complexity of DT building and alleviate the overfitting effect. The embedded FS and CG are based on the notion of fuzzy partition vector (FPV) that comprises the fuzzy membership degrees of every pattern in their target classes, serving as a local evaluation metric with respect to patterns.

Subject recognition based on ground reaction force measurements of gait signals.

An effective subject recognition approach is designed in this paper, using ground reaction force (GRF) measurements of human gait. The method is a three-stage procedure: 1) The original GRF data are translated through wavelet packet (WP) transform in the time-frequency domain. Using a fuzzy-set-based criterion, we determine an optimal WP decomposition, involving feature subspaces with distinguishing gait characteristics.