Xray Image Enhancement: A Technique Combination Approach Medical Xray images are an important and valuable source of studies and diagnoses for diseases with low cost besides its high availability. However, radiological images are subject to degradations related to low contrast and presence of noise. Based on this finding, this article presents a simple but efficient enhancement method for these images with the objective of contrast gain and noise removal. The proposed method (MP) consists of a sequence of interactive steps. Start from the step of double precision conversion and end with removing impulsive noises. An evaluation with the PSNR, Entropy, AMBE, and IQR indicators was performed, besides gain check on the thresholding process and the histogram characterization. The evaluation was conducted on three different datasets in a total of 1409 images chest Xrays. The results compared to others known in the literature proved to be promising and put it as an interesting alternative in the process of enhancement medical Xray images.

UltraDenseNet for LowDose XRay Image Denoising in Cardiac CatheterBased Procedures The continuous development and prolonged use of Xray fluoroscopic imaging in cardiac catheterbased procedures is associated with increasing radiation dose to both patients and clinicians. Reducing the radiation dose leads to increased image noise and artifacts, which may reduce discernable image information. Therefore, advanced denoising methods for lowdose Xray images are needed to improve safety and reliability. Previous Xray imaging denoising methods mainly rely on domain filtration and iterative reconstruction algorithms and some remaining artifacts still appear in the denoised Xray images. Inspired by recent achievements of convolutional neural networks (CNNs) on feature representation in the medical image analysis field, this paper introduces an ultradense denoising network (UDDN) within the CNN framework for Xray image denoising in cardiac catheterbased procedures. After patchbased iterative training, the proposed UDDN achieves a competitive performance in both simulated and clinical cases by achieving higher peak signaltonoise ratio (PSNR) and signaltonoise ratio (SNR) when compared to previous CNN architectures.