Technical Note: MRQy - An open-source tool for quality control of MR imaging data.

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.

A Generalized Ghost Detection and Segmentation Method for Double-Joint Photographic Experts Group Compression.

Background: The versatility of digital photographs and vast usage of image processing tools have made the image manipulation accessible and ubiquitous. Thus, there is an urgent need to develop digital image forensics tools, specifically for joint photographic experts group (JPEG) format which is the most prevailing format for storing digital photographs. Existing double JPEG methods needs improvement to reduce their sensitivity to the random grid shifts which is highly common in manipulation scenario.

Segmentation of dermoscopy images using wavelet networks.

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.

Impulse noise cancellation of medical images using wavelet networks and median filters.

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage.