Simple Muscle Architecture Analysis (SMA): an ImageJ macro tool to automate measurements in B-mode ultrasound scans

In vivo measurements of muscle architecture (i.e. the spatial arrangement of muscle fascicles) are routinely included in research and clinical settings to monitor muscle structure, function and plasticity. However, in most cases such measurements are performed manually, and more reliable and time-efficient automated methods are either lacking completely, or are inaccessible to those without expertise in image analysis. In this work, we propose an ImageJ script to automate the entire analysis process of muscle architecture in ultrasound images: Simple Muscle Architecture Analysis (SMA). Images are filtered in the spatial and frequency domains with built-in commands and external plugins to highlight aponeuroses and fascicles. Fascicle dominant orientation is then computed in regions of interest using the OrientationJ plugin. Bland-Altman plots of analyses performed manually or with SMA indicates that the automated analysis does not induce any systematic bias and that both methods agree equally through the range of measurements. Our test results illustrate the suitability of SMA to analyse images from superficial muscles acquired with a broad range of ultrasound settings.Comment: 8 pages, 7 figures, 1 appendi

Efficient nonlocal-means denoising using the SVD

Nonlocal-means (NL-means) is an image denoising method that replaces each pixel by a weighted average of all the pixels in the image. Unfortunately, the method requires the computation of the weighting terms for all possible pairs of pixels, making it computationally expensive. Some short-cuts assign a weight of zero to any pixel pairs whose neighbourhood averages are too dissimliar. In this paper, we propose an alternative strategy that uses the SVD to more efficiently eliminate pixel pairs that are dissimilar. Experiments comparing this method against other NL-means speed-up strategies show that its refined discrimination between similar and dissimilar pixel neighbourhoods significantly improves the denoising effect. Index Terms — nonlocal-means, denoising, SVD 1

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images

Incluye: artículo, material suplementario, videos y software.Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.We acknowledge the support of the National Institutes of Health grants R35GM124846 (to S.J.) and R01AA028527 (to C.X.), the National Science Foundation grants BIO2145235 and EFMA1830941 (to S.J.), and Marvin H. and Nita S. Floyd Research Fund (to S.J.). This research project was supported, in part, by the Emory University Integrated Cellular Imaging Microscopy Core and by PHS Grant UL1TR000454 from the Clinical and Translational Science Award Program, National Institutes of Health, and National Center for Advancing Translational Sciences.S