Example of Journal of Digital Imaging format
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Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format Example of Journal of Digital Imaging format
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open access Open Access

Journal of Digital Imaging — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Radiology, Nuclear Medicine and Imaging #30 of 288 up up by 79 ranks
Radiological and Ultrasound Technology #8 of 51 up up by 13 ranks
Computer Science Applications #109 of 693 up up by 99 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 453 Published Papers | 3075 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 29/06/2020
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Related Journals

open access Open Access

Springer

Quality:  
High
CiteRatio: 5.1
SJR: 0.6
SNIP: 1.254
open access Open Access

Springer

Quality:  
High
CiteRatio: 5.8
SJR: 1.147
SNIP: 0.995
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 9.0
SJR: 2.558
SNIP: 2.194

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

3.697

44% from 2018

Impact factor for Journal of Digital Imaging from 2016 - 2019
Year Value
2019 3.697
2018 2.572
2017 1.536
2016 1.407
graph view Graph view
table view Table view

6.8

33% from 2019

CiteRatio for Journal of Digital Imaging from 2016 - 2020
Year Value
2020 6.8
2019 5.1
2018 3.3
2017 3.0
2016 3.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 44% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 33% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.055

9% from 2019

SJR for Journal of Digital Imaging from 2016 - 2020
Year Value
2020 1.055
2019 0.967
2018 0.666
2017 0.54
2016 0.657
graph view Graph view
table view Table view

1.846

12% from 2019

SNIP for Journal of Digital Imaging from 2016 - 2020
Year Value
2020 1.846
2019 1.641
2018 1.191
2017 1.076
2016 1.16
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 12% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Digital Imaging

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Springer

Journal of Digital Imaging

The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI?s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and pra...... Read More

Medicine

i
Last updated on
29 Jun 2020
i
ISSN
0897-1889
i
Impact Factor
High - 1.309
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1007/S10278-013-9622-7
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Abstract:

The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their... The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)—an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA. read more read less
View PDF
2,863 Citations
open accessOpen access Journal Article DOI: 10.1007/S10278-004-1014-6
OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images
Antoine Rosset1, Luca Spadola1, Osman Ratib1

Abstract:

A multidimensional image navigation and display software was designed for display and interpretation of large sets of multidimensional and multimodality images such as combined PET-CT studies. The software is developed in Objective-C on a Macintosh platform under the MacOS X operating system using the GNUstep development envi... A multidimensional image navigation and display software was designed for display and interpretation of large sets of multidimensional and multimodality images such as combined PET-CT studies. The software is developed in Objective-C on a Macintosh platform under the MacOS X operating system using the GNUstep development environment. It also benefits from the extremely fast and optimized 3D graphic capabilities of the OpenGL graphic standard widely used for computer games optimized for taking advantage of any hardware graphic accelerator boards available. In the design of the software special attention was given to adapt the user interface to the specific and complex tasks of navigating through large sets of image data. An interactive jog-wheel device widely used in the video and movie industry was implemented to allow users to navigate in the different dimensions of an image set much faster than with a traditional mouse or on-screen cursors and sliders. The program can easily be adapted for very specific tasks that require a limited number of functions, by adding and removing tools from the program’s toolbar and avoiding an overwhelming number of unnecessary tools and functions. The processing and image rendering tools of the software are based on the open-source libraries ITK and VTK. This ensures that all new developments in image processing that could emerge from other academic institutions using these libraries can be directly ported to the OsiriX program. OsiriX is provided free of charge under the GNU open-source licensing agreement at http://homepage.mac.com/rossetantoine/osirix. read more read less

Topics:

Software (55%)55% related to the paper, Image processing (53%)53% related to the paper, Rendering (computer graphics) (52%)52% related to the paper, DICOM (51%)51% related to the paper, Computer graphics (51%)51% related to the paper
1,741 Citations
open accessOpen access Journal Article DOI: 10.1007/S10278-019-00227-X
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
Mohammad Hesam Hesamian1, Wenjing Jia1, Xiangjian He1, Paul J. Kennedy1

Abstract:

Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-... Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions. read more read less

Topics:

Image segmentation (63%)63% related to the paper, Pipeline (software) (53%)53% related to the paper, Component (UML) (50%)50% related to the paper
View PDF
794 Citations
open accessOpen access Journal Article DOI: 10.1007/S10278-017-9983-4
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions
Zeynettin Akkus1, Alfiia Galimzianova2, Assaf Hoogi2, Daniel L. Rubin2, Bradley J. Erickson1

Abstract:

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data As the deep l... Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions Next, the performance, speed, and properties of deep learning approaches are summarized and discussed Finally, we provide a critical assessment of the current state and identify likely future developments and trends read more read less

Topics:

Deep learning (51%)51% related to the paper
View PDF
784 Citations
open accessOpen access Journal Article DOI: 10.1007/BF03178082
Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms.

Abstract:

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized ... The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds Twenty student observers were asked to detect the orientation of the spiculation in the image There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4 The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved read more read less

Topics:

Adaptive histogram equalization (55%)55% related to the paper
554 Citations
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Journal of Digital Imaging format uses SPBASIC citation style.

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Frequently asked questions

1. Can I write Journal of Digital Imaging in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Digital Imaging guidelines and auto format it.

2. Do you follow the Journal of Digital Imaging guidelines?

Yes, the template is compliant with the Journal of Digital Imaging guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Digital Imaging?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Digital Imaging citation style.

4. Can I use the Journal of Digital Imaging templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Digital Imaging.

5. Can I use a manuscript in Journal of Digital Imaging that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Digital Imaging that you can download at the end.

6. How long does it usually take you to format my papers in Journal of Digital Imaging?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Journal of Digital Imaging.

7. Where can I find the template for the Journal of Digital Imaging?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Digital Imaging's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Journal of Digital Imaging's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Journal of Digital Imaging an online tool or is there a desktop version?

SciSpace's Journal of Digital Imaging is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Journal of Digital Imaging?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Journal of Digital Imaging?”

11. What is the output that I would get after using Journal of Digital Imaging?

After writing your paper autoformatting in Journal of Digital Imaging, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Digital Imaging's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Digital Imaging?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Digital Imaging. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Digital Imaging?

The 5 most common citation types in order of usage for Journal of Digital Imaging are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of Digital Imaging?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Digital Imaging's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Journal of Digital Imaging in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Digital Imaging Endnote style according to Elsevier guidelines.

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