Simulation of Skeletal Muscles in Real-Time with Parallel Computing in GPU

Modeling and simulation of the skeletal muscles are usually solved using the Finite Element method (FEM) which, although accurate, commonly needs a complex mesh and the solution is not processed in real-time. In this work, a meshfree model that simulates skeletal muscles considering their functioning and control based on electrical activity, their structure based on biological tissue, and that computes in real-time, is presented. Meshfree methods were used because they are able to surpass most of the limitations that are present in mesh-based methods. The muscular belly was modelled as a particle-based viscoelastic fluid, which is controlled using the monodomain model and shape matching. The smoothed particle hydrodynamics (SPH) method was used to solve both the fluid dynamics and the electrophysiological model. To analyze the accuracy of the method, a similar model was implemented with FEM. Both FEM and SPH methods provide similar solutions of the models in terms of pressure and displacement, with an error of around 0.09, with up to a 10% difference between them. Through the use of General-purpose computing on graphics processing units (GPGPU), real-time simulations that offer a viable alternative to mesh-based models for interactive biological tissue simulations was achieved.Tides Foundation (Grant TFR15-00145

Color-Image Classification Using MRFs for an Outdoor Mobile Robot

In this paper, we suggest to use color-image classification (in several phases) using Markov Random Fields (MRFs) in order to understand natural images from outdoor environment's scenes for a mobile robot. We skip preprocessing phase having same results and better performance. In segmentation phase, we implement a color segmentation method considering I3 color space measure average in little image's cells obtained from a single split step. In classification phase, a MRF was used to identify regions as one of three selected classes; here, we consider at the same time the intrinsic color features of the image and the neighborhood system between image's cells. Finally, we use region growing and contextual information to correct misclassification errors. We have implemented and tested those phases with several images taken at our campus' gardens. We include some results in off-line processing mode and in on-line execution mode on an outdoor mobile robot. The vision system has been used for reactive exploration in an outdoor environment

Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study

In the present research work, image segmentation methods were studied to find internal parameters that provide an efficient identification of the regions of interest in Magnetic Resonance (MR) images used for the therapy planning of High-Intensity Focused Ultrasound (HIFU), a minimally invasive therapeutic method used for selective ablation of tissue. The involved image segmentation methods were threshold, level set and watershed segmentation algorithm with markers (WSAM), and they were applied to transverse and sagittal MR images obtained from an experimental setup of a murine experiment. A parametric study, involving segmentation tests with different values for the internal parameters, was carried out. The F-measure results from the parametric study were analyzed by region using Welch’s ANOVA followed by post hoc Games-Howell test to determine the most appropriate method for region identification. In transverse images, the threshold method had the best performance for the air region with a F-measure median of 0.9802 (0.9743–0.9847, interquartile range IQR 0.0104), the WSAM for the tissue, gel-pad, transducer and water region with a F-measure median of 0.9224 (0.8718–0.9468, IQR 0.075), 0.9553 (0.9496–0.9606, IQR 0.011), 0.9416 (0.9330–0.9540, IQR 0.021) and 0.9769 (0.9741–0.9803, IQR 0.0062), respectively. In sagittal images, threshold method had the best performance for the air region with a F-measure median of 0.9680 (0.9589–0.9735, IQR 0.0146), the WSAM for the tissue and gel-pad regions with a F-measure median of 0.9241 (0.8870–0.9426, IQR 0.0556) and 0.9553 (0.9472–0.9625, IQR 0.0153), respectively, and the Geodesic Active Contours (GAC) method for the transducer and water regions with a F-measure median of 0.9323 (0.9221–0.9402, IQR 0.0181) and 0.9681 (0.9627–0.9715, IQR 0.0088), respectively. The present research work integrates preliminary results to generate more efficient procedures of image segmentation for treatment planning of the MRgHIFU therapy. Future work will address the search of an automatic segmentation process, regardless of the experimental setup. Keyword: F-measure; Ground truth; Image segmentation; MRgHIFU; Non-parametric statisticsConsejo Nacional de Ciencia y Tecnología (Mexico) (Award 419184