Progressive Medical Image Compression using a Diagnostic Quality Measure on Regions-of-Interest

Dealing with lossy compression of medical images requires particular attention whether for still images, video or volumetric slice-sets. In this work we propose an approach based on a selective allocation of coding resources that is directly related to the diagnostic task. We introduce the concepts of Region of Diagnostic Interest (RODI) and Diagnostic Quality as key links between the radiological activities and responsibilities and the functioning of a selective coding algorithm. The coding engine is a modied version of Shapiro's EZW algorithm and the coded bit-stream is fully progressive. The RODI selectivity corresponds to the choice of a set of subband weighting masks that depends on a small set of parameters handled and validated by the radiologist in a very natural manner. In conclusion, we present some experimental results that give interesting insights in favor of using lossy compression in a controlled fashion by a competent physician

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Early detection of capping risk in pharmaceutical compacts

Capping is a common mechanical defect in tablet manufacturing, exhibited during or after the compression process. Predicting tablet capping in terms of process variables (e.g. compaction pressure and speed) and formulation properties is essential in pharmaceutical industry. In current work, a non-destructive contact ultrasonic approach for detecting capping risk in the pharmaceutical compacts prepared under various compression forces and speeds is presented. It is shown that the extracted mechanical properties can be used as early indicators for invisible capping (prior to visible damage). Based on the analysis of X-ray cross-section images and a large set of waveform data, it is demonstrated that the mechanical properties and acoustic wave propagation characteristics is significantly modulated by the tablet’s internal cracks and capping at higher compaction speeds and pressures. In addition, the experimentally extracted properties were correlated to the directly-measured porosity and tensile strength of compacts of Pearlitol®, Anhydrous Mannitol and LubriTose® Mannitol, produced at two compaction speeds and at three pressure levels. The effect compaction speed and pressure on the porosity and tensile strength of the resulting compacts is quantified, and related to the compact acoustic characteristics and mechanical properties. The detailed experimental approach and reported wave propagation data could find key applications in determining the bounds of manufacturing design spaces in the development phase, predicting capping during (continuous) tablet manufacturing, as well as online monitoring of tablet mechanical integrity and reducing batch-to-batch end-product quality variations

Cosmic cookery : making a stereoscopic 3D animated movie.

This paper describes our experience making a short stereoscopic movie visualizing the development of structure in the universe during the 13.7 billion years from the Big Bang to the present day. Aimed at a general audience for the Royal Society's 2005 Summer Science Exhibition, the movie illustrates how the latest cosmological theories based on dark matter and dark energy are capable of producing structures as complex as spiral galaxies and allows the viewer to directly compare observations from the real universe with theoretical results. 3D is an inherent feature of the cosmology data sets and stereoscopic visualization provides a natural way to present the images to the viewer, in addition to allowing researchers to visualize these vast, complex data sets. The presentation of the movie used passive, linearly polarized projection onto a 2m wide screen but it was also required to playback on a Sharp RD3D display and in anaglyph projection at venues without dedicated stereoscopic display equipment. Additionally lenticular prints were made from key images in the movie. We discuss the following technical challenges during the stereoscopic production process; 1) Controlling the depth presentation, 2) Editing the stereoscopic sequences, 3) Generating compressed movies in display speci¯c formats. We conclude that the generation of high quality stereoscopic movie content using desktop tools and equipment is feasible. This does require careful quality control and manual intervention but we believe these overheads are worthwhile when presenting inherently 3D data as the result is signi¯cantly increased impact and better understanding of complex 3D scenes

Image data compression application to imaging spectrometers

The potential of image data compression techniques to satisfy the anticipated requirements of imaging spectrometer missions is discussed. Noiseless coding, rate controlled compression, cluster compression, and error protection are addressed

CURE-OR: Challenging Unreal and Real Environments for Object Recognition

In this paper, we introduce a large-scale, controlled, and multi-platform object recognition dataset denoted as Challenging Unreal and Real Environments for Object Recognition (CURE-OR). In this dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture that are positioned in five different orientations and captured using five devices including a webcam, a DSLR, and three smartphone cameras in real-world (real) and studio (unreal) environments. The controlled challenging conditions include underexposure, overexposure, blur, contrast, dirty lens, image noise, resizing, and loss of color information. We utilize CURE-OR dataset to test recognition APIs-Amazon Rekognition and Microsoft Azure Computer Vision- and show that their performance significantly degrades under challenging conditions. Moreover, we investigate the relationship between object recognition and image quality and show that objective quality algorithms can estimate recognition performance under certain photometric challenging conditions. The dataset is publicly available at https://ghassanalregib.com/cure-or/.Comment: 8 pages, 7 figures, 4 table