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The Journal of Digital imaging is privileged to showcase an excellent commentator this month. Bruce Reiner, MD., a radiologist from the University of Maryland, is writing a series of articles on the evolution of reporting radiological findings. The first of these, “The Challenges, Opportunities, and Imperative of Structured Reporting in Medical Imaging,” gives us a brief history of reporting and introduces the issues involved in moving forward into a structured reporting environment that includes full integration with hospital and radiology information systems, Picture Archiving and Communication Systems, and speech recognition. Future articles will include topics such as Reporting Workflow, Quantifying a Clinical Reporting Preferences for Radiology Reporting, Uncovering the Inherent Deficiencies of Radiology Reporting through Large-scale Data Mining, and a Review article on Structured Reporting Strategies.
We are also publishing a letter to the editor from James Chen and his colleagues from the University of Maryland and the University of Pennsylvania who are commenting on two articles published in December 2008 on the topics of speech recognition and report workflow. I hope this will trigger a healthy and productive debate, perhaps leading to some solutions that will work for most institutions.
Much of the rest of this issue has a major focus on topics of special technical interest to the Imaging Informatics Professional ranging from tools for education, integration, data mining, data collecting, data retrieval, protecting images using watermarking, recording collaborative sessions using DICOM, and creating a patient safety and history questionnaire. We also include an article describing the results of a Pan-Canadian evaluation of lossy compression techniques.
Of special interest to clinical practitioners and the IIP who supports them are articles on computer vision for Ultrasound doppler angle estimation and an automated bone-labeling technique for CT images.
Last, but certainly not least, many researchers will find the article “Image Texture Characterization Using the Discrete Orthonormal S-Transform” interesting and informative. Image texture characterization can be used both to identify tissue and to detect and quantify a disease process or to match similar images in retrieval algorithms.
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License ( https://creativecommons.org/licenses/by-nc/2.0 ), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Honeyman-Buck, J. From the Editor’s Desk. J Digit Imaging 22, 559 (2009). https://doi.org/10.1007/s10278-009-9244-2
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DOI: https://doi.org/10.1007/s10278-009-9244-2