IEEE Transactions on Medical Imaging

Top Research Topics at IEEE Transactions on Medical Imaging?

The scientific interests tackled in IEEE Transactions on Medical Imaging are Artificial intelligence, Computer vision, Iterative reconstruction, Pattern recognition and Algorithm. It focused on Artificial intelligence research but expanded to cover Magnetic resonance imaging. The studies tackled, which mainly focus on Magnetic resonance imaging, apply to Nuclear magnetic resonance as well.

It focuses on Computer vision as well as the interrelated topic of Medical imaging. Iterative reconstruction research featured in it incorporates concerns from various other topics such as Imaging phantom, Tomography, Projection (set theory), Optics and Iterative method. The studies in Pattern recognition featured incorporate elements of Artificial neural network, Deep learning and Contextual image classification.

IEEE Transactions on Medical Imaging dives deep in exploring the relationship between the study of Algorithm and Mathematical optimization. The main emphasis of the journal is the research on Image segmentation, emphasizing the topic of Scale-space segmentation. IEEE Transactions on Medical Imaging connects research in Feature extraction with the related topic of Feature (computer vision).

• Artificial intelligence (60.15%)
• Computer vision (42.01%)
• Iterative reconstruction (26.82%)

What are the most cited papers published in the journal?

• Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm (4987 citations)
• Nonrigid registration using free-form deformations: application to breast MR images (4528 citations)
• Multimodality image registration by maximization of mutual information (4053 citations)

Research areas of the most cited articles at IEEE Transactions on Medical Imaging:

The published articles investigate areas of study like Artificial intelligence, Computer vision, Image segmentation, Image processing and Segmentation. The journal articles focus on Artificial intelligence but the discussions also offer insight into other areas such as Algorithm, Magnetic resonance imaging and Pattern recognition. Computer vision research in the most cited articles connects with the study of Medical imaging.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Internal medicine
• Magnetic resonance imaging

The previous edition focused in particular on these issues:

Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Image segmentation are the subjects of interest in the journal. The research on Artificial intelligence tackled can also make contributions to studies in the areas of Machine learning and Computer vision. The overlapping concepts between Medical imaging and Magnetic resonance imaging are the key highlights of Pattern recognition study.

Some problems in Deep learning that were presented in it overlapped with concepts under Consistency (database systems), Task analysis, Supervised learning, Noise reduction and Iterative reconstruction. The journal focuses on Iterative reconstruction but the discussions also offer insight into other areas such as Image quality, Algorithm and Imaging phantom. It features Segmentation research that overlaps with concepts in Voxel.

The most cited articles from the last journal are:

• Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection (45 citations)
• Super-Resolution Ultrasound Localization Microscopy Through Deep Learning (32 citations)
• Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. (24 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in IEEE Transactions on Medical Imaging (based on the number of publications) are:

• Dinggang Shen (77 papers) published 12 papers at the last edition, 5 less than at the previous edition,
• Jeffrey A. Fessler (70 papers) published 3 papers at the last edition, 3 less than at the previous edition,
• Max A. Viergever (61 papers) absent at the last edition,
• Wiro J. Niessen (48 papers) absent at the last edition,
• Milan Sonka (46 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in IEEE Transactions on Medical Imaging (based on the number of publications) are:

• Stanford University (161 papers) published 15 papers at the last edition, 2 less than at the previous edition,
• Johns Hopkins University (127 papers) published 8 papers at the last edition, 3 less than at the previous edition,
• Harvard University (126 papers) published 7 papers at the last edition, 1 less than at the previous edition,
• University of Pennsylvania (115 papers) published 2 papers at the last edition, 4 less than at the previous edition,
• University of Michigan (114 papers) published 4 papers at the last edition, 6 less than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 9.26% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 18.37% were posted by at least one author from the top 10 institutions publishing in the journal. Another 13.12% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.78% of all publications and 50.73% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Scope and Relevance of a Career in Medical Imaging

Medical imaging research and its related fields are on the rise and are a crucial component of advanced healthcare services. This increased demand has created wonderful career prospects in the field. For those interested in teaching and guiding the next generation of professionals, becoming a history teacher specializing in medical imaging could be a rewarding career choice. However, before embarking on this career path, it is essential to know the specific qualifications and steps required to become a qualified teacher in this field.

In South Dakota, for instance, the process of becoming a history teacher with a focus on medical imaging involves several stages, including earning a bachelor's degree, completing a teacher preparation program, and passing the state's required exams. Further details on the specifications like curriculum, study hours, etc., for becoming a history teacher in this specialized area can be found in history teacher requirements in south dakota. Following these steps diligently can lead to an enriching career in teaching and shaping the medical imaging professionals of the future.

Whether you are a fresh graduate aspiring to enter the medical imaging field or an experienced professional looking to switch careers, understanding the nitty-gritty of becoming a teacher in this specialized domain in South Dakota can give you a competitive edge and pave the way for success.