Example of IEEE Transactions on Medical Imaging format
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Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format
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Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format Example of IEEE Transactions on Medical Imaging format
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open access Open Access
recommended Recommended

IEEE Transactions on Medical Imaging — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Radiological and Ultrasound Technology #2 of 51 up up by 1 rank
Computer Science Applications #21 of 693 -
Software #17 of 389 up up by 3 ranks
Electrical and Electronic Engineering #32 of 693 up up by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1120 Published Papers | 15464 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 15/07/2020
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Related Journals

open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 23.3
SJR: 3.109
SNIP: 3.707
open access Open Access

IEEE

Quality:  
High
CiteRatio: 6.4
SJR: 0.786
SNIP: 2.027
open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.6
SJR: 0.813
SNIP: 1.434

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.

6.685

14% from 2018

Impact factor for IEEE Transactions on Medical Imaging from 2016 - 2019
Year Value
2019 6.685
2018 7.816
2017 6.131
2016 3.942
graph view Graph view
table view Table view

13.8

17% from 2019

CiteRatio for IEEE Transactions on Medical Imaging from 2016 - 2020
Year Value
2020 13.8
2019 16.6
2018 12.2
2017 9.3
2016 8.2
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 14% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 17% 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.

2.322

29% from 2019

SJR for IEEE Transactions on Medical Imaging from 2016 - 2020
Year Value
2020 2.322
2019 3.276
2018 2.188
2017 1.895
2016 1.596
graph view Graph view
table view Table view

3.448

25% from 2019

SNIP for IEEE Transactions on Medical Imaging from 2016 - 2020
Year Value
2020 3.448
2019 4.569
2018 3.544
2017 2.904
2016 2.351
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 29% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 25% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Medical Imaging

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IEEE

IEEE Transactions on Medical Imaging

IEEE Transactions on Medical Imaging (T-MI) encourages the submission of manuscripts on imaging of body structure, morphology and function, including cell and molecular imaging and all forms of microscopy. The journal publishes original contributions on medical imaging achieve...... Read More

Computer Science

i
Last updated on
14 Jul 2020
i
ISSN
0278-0062
i
Impact Factor
Very High - 3.942
i
Acceptance Rate
Not provided
i
Frequency
12
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
IEEEtran
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Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/42.906424
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
Y. Zhang1, J. Michael Brady1, Stephen M. Smith2

Abstract:

The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial informatio... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. Here, the authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. The authors show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, the authors show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation. read more read less

Topics:

Brain segmentation (61%)61% related to the paper, Image segmentation (58%)58% related to the paper, Hidden Markov random field (58%)58% related to the paper, Markov model (57%)57% related to the paper, Hidden Markov model (54%)54% related to the paper
View PDF
6,335 Citations
open accessOpen access Journal Article DOI: 10.1109/42.796284
Nonrigid registration using free-form deformations: application to breast MR images
Daniel Rueckert1, L.I. Sonoda, Carmel Hayes, Derek L. G. Hill, Martin O. Leach, David J. Hawkes

Abstract:

In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form de... In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. read more read less

Topics:

Image registration (59%)59% related to the paper, Affine transformation (57%)57% related to the paper, Breast MRI (54%)54% related to the paper, Free-form deformation (52%)52% related to the paper
View PDF
5,490 Citations
open accessOpen access Journal Article DOI: 10.1109/42.563664
Multimodality image registration by maximization of mutual information
Frederik Maes1, A Collignon1, Dirk Vandermeulen1, Guy Marchal1, Paul Suetens1

Abstract:

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between ... A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications. read more read less

Topics:

Image registration (61%)61% related to the paper, Mutual information (56%)56% related to the paper, Feature extraction (54%)54% related to the paper, Kullback–Leibler divergence (51%)51% related to the paper, Information theory (50%)50% related to the paper
View PDF
4,773 Citations
open accessOpen access Journal Article DOI: 10.1109/42.668698
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
John G. Sled1, Alex P. Zijdenbos, Alan C. Evans

Abstract:

A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available... A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data. read more read less

Topics:

Iterative method (50%)50% related to the paper
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4,613 Citations
Journal Article DOI: 10.1109/TMI.1982.4307558
Maximum Likelihood Reconstruction for Emission Tomography

Abstract:

Previous models for emission tomography (ET) do not distinguish the physics of ET from that of transmission tomography. We give a more accurate general mathematical model for ET where an unknown emission density ? = ?(x, y, z) generates, and is to be reconstructed from, the number of counts n*(d) in each of D detector units d... Previous models for emission tomography (ET) do not distinguish the physics of ET from that of transmission tomography. We give a more accurate general mathematical model for ET where an unknown emission density ? = ?(x, y, z) generates, and is to be reconstructed from, the number of counts n*(d) in each of D detector units d. Within the model, we give an algorithm for determining an estimate ? of ? which maximizes the probability p(n*|?) of observing the actual detector count data n* over all possible densities ?. Let independent Poisson variables n(b) with unknown means ?(b), b = 1, ···, B represent the number of unobserved emissions in each of B boxes (pixels) partitioning an object containing an emitter. Suppose each emission in box b is detected in detector unit d with probability p(b, d), d = 1, ···, D with p(b, d) a one-step transition matrix, assumed known. We observe the total number n* = n*(d) of emissions in each detector unit d and want to estimate the unknown ? = ?(b), b = 1, ···, B. For each ?, the observed data n* has probability or likelihood p(n*|?). The EM algorithm of mathematical statistics starts with an initial estimate ?0 and gives the following simple iterative procedure for obtaining a new estimate ?new, from an old estimate ?old, to obtain ?k, k = 1, 2, ···, ?new(b)= ?old(b) ?Dd=1 n*(d)p(b,d)/??old(b?)p(b?,d),b=1,···B. read more read less
4,288 Citations
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IEEE Transactions on Medical Imaging format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging guidelines and auto format it.

2. Do you follow the IEEE Transactions on Medical Imaging guidelines?

Yes, the template is compliant with the IEEE Transactions on Medical 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 IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging citation style.

4. Can I use the IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging.

5. Can I use a manuscript in IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging that you can download at the end.

6. How long does it usually take you to format my papers in IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging.

7. Where can I find the template for the IEEE Transactions on Medical 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 IEEE Transactions on Medical 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 IEEE Transactions on Medical 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. IEEE Transactions on Medical Imaging an online tool or is there a desktop version?

SciSpace's IEEE Transactions on Medical 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 IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging?”

11. What is the output that I would get after using IEEE Transactions on Medical Imaging?

After writing your paper autoformatting in IEEE Transactions on Medical Imaging, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Medical 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 IEEE Transactions on Medical 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 IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging?

The 5 most common citation types in order of usage for IEEE Transactions on Medical 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 IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Transactions on Medical 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 IEEE Transactions on Medical Imaging Endnote style according to Elsevier guidelines.

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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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