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. 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.

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. Also, a fully automatic pipeline, in terms of segmentation followed by the classifier is still required. METHODS: First, a low-pass filter (with some modifications) is used to distinguish between high-textured and low-textured areas. Then, using the inconsistency values between the quality-factors, a grayscale image, called the ghost image, is constituted. To automate the whole method, a novel segmentation method is also proposed, which extracts the ghost borders. In the last step of the proposed method, using Kolmogorov-Smirnov statistic, the distance between two separated areas (ghost area and the rest of the image) is calculated and compared with a predefined threshold to confirm the presence of forgery/authenticity. RESULTS: In this study, a simple yet efficient algorithm to detect double-JPEG compression is proposed. This method reveals the sub-visual differences in the quality factor in the different parts of the image. Afterward, forgery borders are extracted and are used to assess authenticity score. In our experiments, the average specificity of our segmentation method exceeds 92% and the average precision is 75%. CONCLUSION: The final binary results for classification are compared with six state-of-the-art methods. According to several performance metrics, our method outperforms the previously proposed ones.

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. 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.

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. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well.