Ensuring the regularity and correctness of rehabilitation exercises in the home environment is a prerequisite for successful treatment. This clinical study compares balance therapy in the home environment on a conventional balance mat and an instrumented wobble board, with biofeedback supported by a rehabilitation scheme realized as web-based software that controls the course of rehabilitation remotely. The study included 55 patients with knee injuries. The control group consisted of 25 patients (12 females and 13 males, mean age 39 ± 12 years) and the study group of 30 patients (19 females and 11 males, mean age 40 ± 12 years). Treatment effects were compared using the ICS Balance Platform measurement system. Measurements showed significant differences in the change in ICS Balance platform parameters representing the dynamic stability of the patients. The dynamic stability improved more with the instrumented wobble board. The study did not show an influence of different methods of communication with patients during home-based rehabilitation.

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.