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.

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.

Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction.

A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics.

A stable learning algorithm for block-diagonal recurrent neural networks: application to the analysis of lung sounds.

A novel learning algorithm, the Recurrent Neural Network Constrained Optimization Method (RENNCOM) is suggested in this paper, for training block-diagonal recurrent neural networks. The training task is formulated as a constrained optimization problem, whose objective is twofold: (1) minimization of an error measure, leading to successful approximation of the input/output mapping and (2) optimization of an additional functional, the payoff function, which aims at ensuring network stability throughout the learning process. Having assured the network and training stability conditions, the payoff function is switched to an alternative form with the scope to accelerate learning.

A dynamic fuzzy neural filter for separation of discontinuous adventitious sounds from vesicular sounds.

This paper presents a recurrent filter that performs real-time separation of discontinuous adventitious sounds from vesicular sounds. The filter uses two Dynamic Fuzzy Neural Networks, operating in parallel, to perform the task of separation of the lung sounds, obtained from patients with pulmonary pathology. Extensive experimental results, including fine/coarse crackles and squawks, are given, and a performance comparison with a series of other models is conducted, underlining the separation capabilities of the proposed filter and its improved performance with respect to its competing rivals.