Example of Medical Image Analysis format
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Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format
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Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format Example of Medical Image Analysis format
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open access Open Access ISSN: 13618415 e-ISSN: 13618423
recommended Recommended

Medical Image Analysis — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Radiology, Nuclear Medicine and Imaging #1 of 288 up up by 7 ranks
Health Informatics #1 of 95 -
Computer Graphics and Computer-Aided Design #1 of 88 up up by 2 ranks
Radiological and Ultrasound Technology #1 of 51 -
Computer Vision and Pattern Recognition #4 of 85 up up by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 571 Published Papers | 13828 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 13/06/2020
Insights & related journals
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Top papers
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor 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.

11.148

26% from 2018

Impact factor for Medical Image Analysis from 2016 - 2019
Year Value
2019 11.148
2018 8.88
2017 5.356
2016 4.188
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 26% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

24.2

41% from 2019

CiteRatio for Medical Image Analysis from 2016 - 2020
Year Value
2020 24.2
2019 17.2
2018 12.1
2017 9.6
2016 10.1
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 41% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

2.887

26% from 2019

SJR for Medical Image Analysis from 2016 - 2020
Year Value
2020 2.887
2019 3.877
2018 2.452
2017 1.928
2016 1.948
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 26% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

5.246

2% from 2019

SNIP for Medical Image Analysis from 2016 - 2020
Year Value
2020 5.246
2019 5.351
2018 3.808
2017 2.854
2016 2.72
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

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CiteRatio: 5.3 | SJR: 0.701 | SNIP: 1.408
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Medical Image Analysis

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Elsevier

Medical Image Analysis

Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging prob...... Read More

Medicine

i
Last updated on
13 Jun 2020
i
ISSN
1361-8415
i
Impact Factor
Very High - 4.188
i
Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
elsarticle-num
i
Citation Type
Author Year
(Blonder et al., 1982)
i
Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M., 1982. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25, 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1016/J.MEDIA.2017.07.005
A survey on deep learning in medical image analysis
01 Dec 2017 - Medical Image Analysis

Abstract:

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We surve... Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research. read more read less

Topics:

Deep learning (53%)53% related to the paper
View PDF
5,977 Citations
Journal Article DOI: 10.1016/S1361-8415(01)00036-6
A global optimisation method for robust affine registration of brain images
Mark Jenkinson1, Stephen M. Smith1
01 Jun 2001 - Medical Image Analysis

Abstract:

Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework — one of optimising a cost functi... Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework — one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage. read more read less

Topics:

Image registration (58%)58% related to the paper
5,642 Citations
Journal Article DOI: 10.1016/S1361-8415(01)80026-8
A survey of medical image registration.
J. B. Antoine Maintz1, Max A. Viergever1
01 Mar 1998 - Medical Image Analysis

Abstract:

The purpose of this paper is to present a survey of recent (published in 1993 or later) publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods. The statistics of ... The purpose of this paper is to present a survey of recent (published in 1993 or later) publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods. The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is based on either segmented points or surfaces, or on techniques endeavouring to use the full information content of the images involved. read more read less

Topics:

Image registration (54%)54% related to the paper
View PDF
3,351 Citations
open accessOpen access Journal Article DOI: 10.1016/J.MEDIA.2007.06.004
Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.
Brian B. Avants1, Charles L. Epstein1, Murray Grossman1, James C. Gee1
01 Feb 2008 - Medical Image Analysis

Abstract:

One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD)... One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals. read more read less

Topics:

Large deformation diffeomorphic metric mapping (52%)52% related to the paper, Population (52%)52% related to the paper
3,190 Citations
Journal Article DOI: 10.1016/S1361-8415(96)80007-7
Deformable models in medical image analysis: a survey
Tim McInerney1, Demetri Terzopoulos1
01 Jun 1996 - Medical Image Analysis

Abstract:

This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics and approximation theory. They have proven to be effective in ... This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics and approximation theory. They have proven to be effective in segmenting, matching and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking. read more read less
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2,186 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

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With SciSpace, you do not need a word template for Medical Image Analysis.

It automatically formats your research paper to Elsevier formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

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Medical Image Analysis format uses elsarticle-num citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Medical Image Analysis guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Medical Image Analysis citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Medical Image Analysis's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

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 those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo 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.

The 5 most common citation types in order of usage are:.

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

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Medical Image Analysis.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Medical Image Analysis Endnote style, according to elsevier guidelines.

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Typset automatically formats your research paper to Medical Image Analysis formatting guidelines and citation style.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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