this study is the first to perform wound bioprinting based on image segmentation. It also compares several segmentation methods used for this purpose to determine the best.

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional -penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions.

Purpose: The aim of this study was to determine the efficacy of compressed sensing reconstructions for specific clinical neuroimaging applications of magnetic resonance imaging beyond more conventional k-space under-sampling approaches such as parallel imaging and simple low-resolution acquisitions.Methods: Four routine clinical neuroimaging pulse sequences were chosen for this study due to their long acquisition time. In a series of blinded studies, three board-certified radiologists independently evaluated compressed sensing, parallel imaging, and low-resolution images at up to 5x accelerations. Experiments on synthetic brain images with artificial but realistic lesions were carried out to assess diagnostic accuracy for the detection of non-specific white matter lesions, permitting controlled evaluation of shift-variant compressed sensing reconstructions.Results: Ringing and blurring were identified as the primary artifacts that hinder diagnostic quality of combined compressed sensing and parallel imaging reconstructions. The findings indicate that up to 5x acceleration is possible by a combined compressed sensing and parallel imaging reconstruction. However, efficacy of compressed sensing reconstructions as well as the improvement in image quality over the more conventional parallel imaging and low-resolution acquisitions appear to vary with pulse sequence.Conclusion: Mild to moderate accelerations are possible for those sequences by a combined compressed sensing and parallel imaging reconstruction while maintaining diagnostic quality of reconstructions. Nevertheless, for certain sequences/applications one might mildly reduce the acquisition time by appropriately reducing the imaging resolution while maintaining diagnostic quality/accuracy, rather than the more complicated compressed sensing reconstruction.

Recently, merging signal processing techniques with information security services has found a lot of attention. Steganography and steganalysis are among those trends. Like their counterparts in cryptology, steganography and steganalysis are in a constant battle. Steganography methods try to hide the presence of covert messages in innocuouslooking data, whereas steganalysis methods try to break steganography algorithms and reveal the existence of hidden messages. The stream nature of audio signals, their popularity, and their wide spread usage make them very good candidates for steganography. This has led to a very rich literature on both steganography and steganalysis of audio signals. This paper intends to conduct a comprehensive review of audio steganalysis methods aggregated over near fifteen years. To that end, both compressed and con-compressed methods are reviewed, and then their important details are presented in different tables. Furthermore, some of the most recent audio steganalysis methods (both non-compressed and compressed ones) are implemented and comparative analyses on their performances are conducted. Finally, the paper provides some possible directions for future researches on audio steganalysis.