This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.
The Achilles tendon (AT) is the most frequently ruptured tendon in the human body yet the aetiology remains poorly understood. Despite the extensively published literature, controversy still surrounds the optimum treatment of complete rupture. Both non-operative management and percutaneous repair are attractive alternatives to open surgery, which carries the highest complication and cost profile. However, the lack of a universally accepted scoring system has limited any evaluation of treatment options. A typical UK district general hospital treats approximately 3 cases of AT rupture a month. It is therefore important for orthopaedic surgeons to correctly diagnose and treat these injuries with respect to the best current evidence-based practice. In this review article, we discuss the relevant pathophysiology and diagnosis of the ruptured AT and summarize the current evidence for treatment.
We report a novel method for achieving consistent liquid phase solvent bonding of plastic microfluidic devices via the use of retention grooves at the bonding interface. The grooves are patterned during the regular microfabrication process, and can be placed at the periphery of a device, or surrounding microfluidic features with open ports, where they effectively mitigate solvent evaporation, and thus substantially reduce poor bond coverage. This method is broadly applicable to a variety of plastics and solvents, and produces devices with high bond quality (i.e., coverage, strength, and microfeature fidelity) that are suitable for studies in physics, chemistry, and cell biology at the microscale.
Purpose There is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development and imaging research. To ensure development of generalizable computerized models, there is a need to quickly determine relative quality differences in these cohorts, especially when considering MRI datasets which can exhibit wide variations in image appearance. The purpose of this study is to present a quantitative quality control tool, MRQy, to help interrogate MR imaging datasets for: (a) site‐ or scanner‐specific variations in image resolution or image contrast, and (b) imaging artifacts such as noise or inhomogeneity; which need correction prior to model development. Methods Unlike existing imaging quality control tools, MRQy has been generalized to work with images from any body region to efficiently extract a series of quality measures (e.g., noise ratios, variation metrics) and MR image metadata (e.g., voxel resolution and image dimensions). MRQy also offers a specialized HTML5‐based front‐end designed for real‐time filtering and trend visualization of quality measures. Results MRQy was used to evaluate (a) n = 133 brain MRIs from TCIA (7 sites) and (b) n = 104 rectal MRIs (3 local sites). MRQy measures revealed significant site‐specific variations in both cohorts, indicating potential batch effects. Before processing, MRQy measures could be used to identify each of the seven sites within the TCIA cohort with 87.5%, 86.4%, 90%, 93%, 90%, 60%, and 92.9% accuracy and the three sites within the rectal cohort with 91%, 82.8%, and 88.9% accuracy using unsupervised clustering. After processing, none of the sites could be distinctively clustered via MRQy measures in either cohort; suggesting that batch effects had been largely accounted for. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common acquisition artifacts. Conclusions MRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible and open‐source at http://github.com/ccipd/MRQy for community use and feedback.
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