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Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology

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https://www.readbyqxmd.com/read/27904975/rayplus-a-web-based-platform-for-medical-image-processing
#1
Rong Yuan, Ming Luo, Zhi Sun, Shuyue Shi, Peng Xiao, Qingguo Xie
Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation...
November 30, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27896452/optimization-of-image-quality-and-dose-in-digital-mammography
#2
Agnes M F Fausto, M C Lopes, M C de Sousa, Tânia A C Furquim, Anderson W Mol, Fermin G Velasco
Nowadays, the optimization in digital mammography is one of the most important challenges in diagnostic radiology. The new digital technology has introduced additional elements to be considered in this scenario. A major goal of mammography is related to the detection of structures on the order of micrometers (μm) and the need to distinguish the different types of tissues, with very close density values. The diagnosis in mammography faces the difficulty that the breast tissues and pathological findings have very close linear attenuation coefficients within the energy range used in mammography...
November 28, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27896451/transfer-learning-with-convolutional-neural-networks-for-classification-of-abdominal-ultrasound-images
#3
Phillip M Cheng, Harshawn S Malhi
The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256 × 256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set...
November 28, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27844218/microcalcification-segmentation-from-mammograms-a-morphological-approach
#4
Marcin Ciecholewski
This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones...
November 14, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27844217/assessing-inaccuracies-in-automated-information-extraction-of-breast-imaging-findings
#5
Ronilda Lacson, Martha E Goodrich, Kimberly Harris, Phyllis Brawarsky, Jennifer S Haas
We previously identified breast imaging findings from radiology reports using an expert-based information extraction algorithm as part of the National Cancer Institute's Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative. We validate this algorithm and assess inaccuracies in a different institutional setting. Mammography, ultrasound (US), and breast magnetic resonance imaging (MRI) reports of patients at an academic health system between 4/2013 and 6/2013 were included for analysis...
November 14, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27844216/review-of-docphin-an-app-for-mobile-access-to-medical-journals
#6
EDITORIAL
Andy Wai Kan Yeung, W Keung Leung
No abstract text is available yet for this article.
November 14, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27844215/review-of-radiology-app-for-android-by-the-radiological-society-of-north-america-rsna
#7
EDITORIAL
Andy Wai Kan Yeung, W Keung Leung
No abstract text is available yet for this article.
November 14, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27844214/app-review-series-radiology-pocket-game
#8
EDITORIAL
V B Surya Prasath
No abstract text is available yet for this article.
November 14, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27834027/an-automatic-image-processing-workflow-for-daily-magnetic-resonance-imaging-quality-assurance
#9
Juha I Peltonen, Teemu Mäkelä, Alexey Sofiev, Eero Salli
The performance of magnetic resonance imaging (MRI) equipment is typically monitored with a quality assurance (QA) program. The QA program includes various tests performed at regular intervals. Users may execute specific tests, e.g., daily, weekly, or monthly. The exact interval of these measurements varies according to the department policies, machine setup and usage, manufacturer's recommendations, and available resources. In our experience, a single image acquired before the first patient of the day offers a low effort and effective system check...
November 10, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27832519/automatic-estimation-of-volumetric-breast-density-using-artificial-neural-network-based-calibration-of-full-field-digital-mammography-feasibility-on-japanese-women-with-and-without-breast-cancer
#10
Jeff Wang, Fumi Kato, Hiroko Yamashita, Motoi Baba, Yi Cui, Ruijiang Li, Noriko Oyama-Manabe, Hiroki Shirato
Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer...
November 10, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27832518/expanding-the-scope-of-an-automated-radiology-recommendation-tracking-engine-initial-experiences-and-lessons-learned
#11
Mindy Y Licurse, Darco Lalevic, Hanna M Zafar, Mitchell D Schnall, Tessa S Cook
An automated radiology recommendation-tracking engine for incidental focal masses in the liver, pancreas, kidneys, and adrenal glands was launched within our institution in July 2013. For 2 years, the majority of CT, MR, and US examination reports generated within our health system were mined by the engine. However, the need to expand the system beyond the initial four organs was soon identified. In July 2015, the second phase of the system was implemented and expanded to include additional anatomic structures in the abdomen and pelvis, as well as to provide non-radiology and non-imaging options for follow-up...
November 10, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27826671/a-feasibility-study-of-telementoring-for-identifying-the-appendix-using-smartphone-based-telesonography
#12
Yoonje Lee, Changsun Kim, Hyuk Joong Choi, Bossng Kang, Jaehoon Oh, Tae Ho Lim
We investigated the feasibility of the clinical application of novice-practitioner-performed/offsite-mentor-guided ultrasonography for identifying the appendix. A randomized crossover study was conducted using a telesonography system that can transmit the ultrasound images displayed on the ultrasound monitor (ultrasound sequence video) and images showing the practitioner's operations (background video) to a smartphone without any interruption in motion over a Long-Term Evolution (LTE) network. Thirty novice practitioners were randomly assigned to two groups...
November 8, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27798745/diagnostic-accuracy-and-visual-search-efficiency-single-8%C3%A2-mp-vs-dual-5%C3%A2-mp-displays
#13
Elizabeth A Krupinski
This study compared a single 8 MP vs. dual 5 MP displays for diagnostic accuracy, reading time, number of times the readers zoomed/panned images, and visual search. Six radiologists viewed 60 mammographic cases, once on each display. A sub-set of 15 cases was viewed in a secondary study using eye-tracking. For viewing time, there was significant difference (F = 13.901, p = 0.0002), with 8 MP taking less time (62.04 vs. 68.99 s). There was no significant difference (F = 0.254, p = 0.6145) in zoom/pan use (1...
October 31, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27785632/building-and-querying-rdf-owl-database-of-semantically-annotated-nuclear-medicine-images
#14
Kyung Hoon Hwang, Haejun Lee, Geon Koh, Debra Willrett, Daniel L Rubin
As the use of positron emission tomography-computed tomography (PET-CT) has increased rapidly, there is a need to retrieve relevant medical images that can assist image interpretation. However, the images themselves lack the explicit information needed for query. We constructed a semantically structured database of nuclear medicine images using the Annotation and Image Markup (AIM) format and evaluated the ability the AIM annotations to improve image search. We created AIM annotation templates specific to the nuclear medicine domain and used them to annotate 100 nuclear medicine PET-CT studies in AIM format using controlled vocabulary...
October 26, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27783172/nsf-vs-cin-aggregated-screening-safety-and-protocol-tools-for-contrast-imaging-in-the-setting-of-renal-insufficiency
#15
EDITORIAL
James Shin
No abstract text is available yet for this article.
October 25, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27766443/improved-screening-mammogram-workflow-by-maximizing-pacs-streamlining-capabilities-in-an-academic-breast-center
#16
Ramya Pham, Daniel Forsberg, Donna Plecha
The aim of this study was to perform an operational improvement project targeted at the breast imaging reading workflow of mammography examinations at an academic medical center with its associated breast centers and satellite sites. Through careful analysis of the current workflow, two major issues were identified: stockpiling of paperwork and multiple worklists. Both issues were considered to cause significant delays to the start of interpreting screening mammograms. Four workflow changes were suggested (scanning of paperwork, worklist consolidation, use of chat functionality, and tracking of case distribution among trainees) and implemented in July 2015...
October 20, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27730417/high-throughput-classification-of-radiographs-using-deep-convolutional-neural-networks
#17
Alvin Rajkomar, Sneha Lingam, Andrew G Taylor, Michael Blum, John Mongan
The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27730416/cyber-security-issues-in-healthcare-information-technology
#18
Steve G Langer
In 1999-2003, SIIM (then SCAR) sponsored the creation of several special topic Primers, one of which was concerned with computer security. About the same time, a multi-society collaboration authored an ACR Guideline with a similar plot; the latter has recently been updated. The motivation for these efforts was the launch of Health Information Portability and Accountability Act (HIPAA). That legislation directed care providers to enable the portability of patient medical records across authorized medical centers, while simultaneously protecting patient confidentiality among unauthorized agents...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27730415/machine-learning-interface-for-medical-image-analysis
#19
Yi C Zhang, Alexander C Kagen
TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/27730414/survey-of-non-rigid-registration-tools-in-medicine
#20
András P Keszei, Benjamin Berkels, Thomas M Deserno
We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
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