Presented is a new computer-aided multispectral image processing method which is used in three spatial dimensions and one spectral dimension where the dynamic, contrast enhanced magnetic resonance parameter maps derived from voxel-wise model-fitting represent the spectral dimension. The method is based on co-occurrence analysis using a 3-D window of observation which introduces an automated identification of suspicious lesions. The co-occurrence analysis defines 21 different statistical features, a subset of which were input to a neural network classifier where the assessments of the voxel-wise majority of a group of radiologist readings were used as the gold standard. The voxel-wise true positive fraction (TPF) and false positive fraction (FPF) results of the computer classifier were statistically indistinguishable from the TPF and FPF results of the readers using a one sample paired t-test. In order to observe the generality of the method, two different groups of studies were used with widely different image acquisition specifications.

Abstract Texture analysis is one possible method of detecting features in biomedical images. During texture analysis, texture‐related information is found by examining local variations in image brightness. Four‐dimensional (4D) Haralick texture analysis is a method that extracts local variations along space and time dimensions and represents them as a collection of 14 statistical parameters. However, application of the 4D Haralick method on large time‐dependent image datasets is hindered by data retrieval, computation, and memory requirements. This paper describes a parallel implementation using a distributed component‐based framework of 4D Haralick texture analysis on PC clusters. The experimental performance results show that good performance can be achieved for this application via combined use of task‐ and data‐parallelism. In addition, we show that our 4D texture analysis implementation can be used to classify imaged tissues. Copyright © 2006 John Wiley & Sons, Ltd.

Texture analysis is one possible method to detect features in biomedical images. During texture analysis, texture related information is found by examining local variations in image brightness. 4-dimensional (4D) Haralick texture analysis is a method that extracts local variations along space and time dimensions and represents them as a collection of fourteen statistical parameters. However, the application of the 4D Haralick method on large time-dependent 2D and 3D image datasets is hindered by computation and memory requirements. This paper presents a parallel implementation of 4D Haralick texture analysis on PC clusters. We present a performance evaluation of our implementation on a cluster of PCs. Our results show that good performance can be achieved for this application via combined use of task- and data-parallelism.

Rhythmic movements of a five-link sagittal biped with muscle-like actuators are considered. In walking contact is periodically made with the environment as the support phases change. The inputs to every actuator are modeled after the inputs to muscles in mammals. The system possesses intrinsic position and velocity feedback due to the actuator dynamics. A control strategy is articulated that is novel in that (a) it is physiologically viable, (b) it simplifies the dynamics, and (c) it adapts to speed of walking, going up and down stairs, going up or dozen inclines, maneuvering above obstacles or holes, and the tempo and stride length of walking. Simulations of the walk of a five-link sagittal biped are presented.

Control strategies for a one-link model of the human forearm system are presented. Three attributes of the human forearm are implemented in the second order nonlinear model: neural transmission delays in the feedback paths, nonlinear behavior of the spindle reflex, and stiffness regulation through coactivation. Three feedback loops are present in the model: intrinsic feedback (undelayed) from the actuators, spindle feedback (delayed), and higher level controller feedback (delayed). The stability of the model is examined through computer simulation analysis. A method of speed control of the arm is presented that utilizes continuous transitions in the feedforward activation levels of the muscles. Two control strategies are utilized by the higher level controller: a proportional plus integral (PI) compensation strategy, and a fuzzy control strategy. Both strategies can endure long loop transmission delays without causing the system to become unstable. A comparison of the settling time of these two controllers in compensating for disturbances and loading errors is presented.>