An enhanced median filter for removing noise from MR images

In this paper, a novel decision based median (DBM) filter for enhancing MR images has been proposed. The method is based on eliminating impulse noise from MR images. A median-based method to remove impulse noise from digital MR images has been developed. Each pixel is leveled from black to white like gray-level. The method is adjusted in order to decide whether the median operation can be applied on a pixel. The main deficiency in conventional median filter approaches is that all pixels are filtered with no concern about healthy pixels. In this research, to suppress this deficiency, noisy pixels are initially detected, and then the filtering operation is applied on them. The proposed decision method (DM) is simple and leads to fast filtering. The results are more accurate than other conventional filters. Moreover, DM adjusts itself based on the conditions of local detections. In other words, DM operation on detecting a pixel as a noise depends on the previous decision. As a considerable advantage, some unnecessary median operations are eliminated and the number of median operations reduces drastically by using DM. Decision method leads to more acceptable results in scenarios with high noise density. Furthermore, the proposed method reduces the probability of detecting noise-free pixels as noisy pixels and vice versa

Recent trends in molecular diagnostics of yeast infections : from PCR to NGS

The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside