The idea for softcopy viewing of medical image outside the radiology reading room spread among the scientists in various fields for several years. An image could be read on workstation of all types, from desktop across movable to handheld. Benefits are numerous and continue to grow as physicians use them discovering new usage cases. Proposed solutions vary with PACS architecture invasion level, communication and storage image formats, and utilization. We employ JPEG2000 standard because of its high (lossy/lossless) compression ratio with minimal spatial distortion, retrieval-oriented storage, and streaming. It is embedded in PACS as the DICOM Private Data Element containing JPIP parameter string, so-called DICOM2000. The DICOM2000 message is transparent for standard DICOM devices at the slightest level of invasion. Thanks to sophisticated JPEG2000 streaming, medical image becomes suitable for any resolution and quality display and (wireless) networks. The solution is validated on the ACR/NEMA standard test set of PACS images.
Power quality disturbances (PQD) have a negative impact on power quality-sensitive equipment, often resulting in great financial losses. To prevent these losses, besides detecting a PQD on time, it is important to classify it, so that appropriate recovery procedures are employed. The majority of research employs machine learning model PQD classifiers on manually extracted features from simulated or real-world signals. This paper presents an end-to-end approach that circumvents the manual feature extraction and uses signals generated from mathematical voltage sag type formulas. We developed a configurable voltage sag generator that was used to form training and validation datasets. Based on the synthetic three-phase voltage signals, we trained several end-to-end LSTM classifiers that classify voltage sags according to ABC classification. The best-performing model achieved an accuracy of over 90% in the real-world dataset.
In this paper we present a continuation of our work on three dimensional quality evaluation space for medical image compression. Quality dimensions are: presentation-objective, presentation-subjective, and technical-objective. Each quality dimension is defined by a quality vector. We present which attributes of medical image compression should be included in quality vectors.I.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.