Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging

Bidgood, W. Dean; Horii, Steven C.; Prior, Fred; Syckle, Donald E. Van

doi:10.1136/jamia.1997.0040199

Cited by 321 publications

(85 citation statements)

References 2 publications

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“…The system should automatically receive Digital Imaging and Communications in Medicine (DICOM) files [60] of the exams, and process and store the results in standard files;…”

Section: Brief Definition Of a Cade Systemmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

200

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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“…The system should automatically receive Digital Imaging and Communications in Medicine (DICOM) files [60] of the exams, and process and store the results in standard files;…”

Section: Brief Definition Of a Cade Systemmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

200

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“…Regarding 3D viewing, OsiriX Lite and MicroView fulfill the most criteria of standalone (both 5 criteria), Xero Conquest, DCM4CHEE or Orthanc PACS for WADO required, 7 comprehensive documentation in wiki, 8 Forum offline, 9 Displays only first slice of test data, 10 Using startup macro feature, 11 Saves annotations in other formats, but not DICOM, 12 Requires Oviyam, 13 Designated, but not working, 14 Annotated objects could not be reopened, 15 Requires DCM4CHEE PACS, 16 Runs only with Google Chrome, Images cannot be stored, 34 Seems to be not working correctly in demo (3 criteria) of web-based, and ImageJ as well as MIPAV (both 5 criteria) of platform-independent viewers.…”

Section: Resultsmentioning

confidence: 99%

“…C5 -C-STORE SCP DICOM C-STORE service class provider (SCP) is an operation that allows transfer and storage of DICOM objects into connected systems [1,16]. In case of the role as SCP, the viewer passively receives data from other DICOM nodes (e.g., PACS).…”

Section: Interfacesmentioning

confidence: 99%

A Survey of DICOM Viewer Software to Integrate Clinical Research and Medical Imaging

2015

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The digital imaging and communications in medicine (DICOM) protocol is the leading standard for image data management in healthcare. Imaging biomarkers and image-based surrogate endpoints in clinical trials and medical registries require DICOM viewer software with advanced functionality for visualization and interfaces for integration. In this paper, a comprehensive evaluation of 28 DICOM viewers is performed. The evaluation criteria are obtained from application scenarios in clinical research rather than patient care. They include (i) platform, (ii) interface, (iii) support, (iv) twodimensional (2D), and (v) three-dimensional (3D) viewing. On the average, 4.48 and 1.43 of overall 8 2D and 5 3D image viewing criteria are satisfied, respectively. Suitable DICOM interfaces for central viewing in hospitals are provided by GingkoCADx, MIPAV, and OsiriX Lite. The viewers ImageJ, MicroView, MIPAV, and OsiriX Lite offer all included 3D-rendering features for advanced viewing. Interfaces needed for decentral viewing in web-based systems are offered by Oviyam, Weasis, and Xero. Focusing on open source components, MIPAV is the best candidate for 3D imaging as well as DICOM communication. Weasis is superior for workflow optimization in clinical trials. Our evaluation shows that advanced visualization and suitable interfaces can also be found in the open source field and not only in commercial products.

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“…The first is formats intended to standardize the images generated by diagnostic modalities, e.g., Dicom [4]. The second is formats born with the aim to facilitate and strengthen postprocessing analysis, e.g., Analyze [5], Nifti [6], and Minc [7].…”

Section: File Formatsmentioning

confidence: 99%

Medical Image File Formats

2013

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Image file format is often a confusing aspect for someone wishing to process medical images. This article presents a demystifying overview of the major file formats currently used in medical imaging: Analyze, Neuroimaging Informatics Technology Initiative (Nifti), Minc, and Digital Imaging and Communications in Medicine (Dicom). Concepts common to all file formats, such as pixel depth, photometric interpretation, metadata, and pixel data, are first presented. Then, the characteristics and strengths of the various formats are discussed. The review concludes with some predictive considerations about the future trends in medical image file formats.

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Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging

Cited by 321 publications

References 2 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

A Survey of DICOM Viewer Software to Integrate Clinical Research and Medical Imaging

Medical Image File Formats

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