Journal of Digital Imaging

Top Research Topics at Journal of Digital Imaging?

The journal mainly tackles studies in Artificial intelligence, Computer vision, Radiology, Multimedia and Picture archiving and communication system. The Artificial intelligence study tackled is a key component of adjacent topics in the area of Pattern recognition. The Computer vision works featured in it incorporate elements from Mammography and Computer graphics (images).

Most of the Radiology studies addressed also intersect with Nuclear medicine. Multimedia research featured in Journal of Digital Imaging incorporates concerns from various other topics such as DICOM, World Wide Web, The Internet, Radiology information systems and Workstation. In the journal, Software and Medical imaging are investigated in conjunction with one another to address concerns in DICOM research.

Journal of Digital Imaging investigates Medical imaging research which frequently intersects with Medical physics. Journal of Digital Imaging addresses concerns in Picture archiving and communication system which are intertwined with other disciplines, such as Workflow and Communications system. The majority of Segmentation studies are focused on the issues of Image segmentation.

• Artificial intelligence (31.66%)
• Computer vision (20.83%)
• Radiology (18.05%)

What are the most cited papers published in the journal?

• The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository (1516 citations)
• OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images (1464 citations)
• Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions (454 citations)

Research areas of the most cited articles at Journal of Digital Imaging:

The most cited articles mainly tackle studies in Artificial intelligence, Computer vision, Radiology, Pattern recognition and Image processing. The most cited papers cover various topics on Artificial intelligence such as Deep learning, Segmentation, Convolutional neural network, Medical imaging and Artificial neural network. The journal publications focus on Medical imaging but sometimes tackle the closely related topic of Multimedia which is concerned with World Wide Web, Software and Teleradiology.

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

• Internal medicine
• Artificial intelligence
• Radiology

The previous edition focused in particular on these issues:

The journal mostly deals with topics like Artificial intelligence, Pattern recognition, Deep learning, Radiology and Segmentation. Topics in Artificial intelligence explored in the journal were investigated in conjunction with research in Machine learning and Receiver operating characteristic. Research in Image (mathematics) and the interrelating topic of Feature (computer vision) were among the subjects of interest in the Pattern recognition studies discussed in the journal.

Journal of Digital Imaging investigates Deep learning in the context of the closely related subject of areas like

• Radiography which connect with Cross-validation and Nuclear medicine,
• Computer-aided most often made with reference to Mammography.. The journal facilitates discussions in Computed tomography as part of the larger field of Radiology, however, it also tackles fields such as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The journal explores research in Segmentation alongside concepts in Magnetic resonance imaging and other areas of study in Reproducibility.

The most cited articles from the last journal are:

• Unsupervised Deep Anomaly Detection in Chest Radiographs (8 citations)
• Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes. (7 citations)
• Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization (5 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 Journal of Digital Imaging (based on the number of publications) are:

• Bruce I. Reiner (72 papers) absent at the last edition,
• Bradley J. Erickson (54 papers) published 2 papers at the last edition,
• Steve G. Langer (43 papers) published 1 paper at the last edition,
• Eliot L. Siegel (41 papers) absent at the last edition,
• Katherine P. Andriole (40 papers) published 3 papers at the last edition, 2 more than at the previous 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 Journal of Digital Imaging (based on the number of publications) are:

• Mayo Clinic (120 papers) published 5 papers at the last edition, 3 more than at the previous edition,
• University of Washington (61 papers) absent at the last edition,
• Veterans Health Administration (60 papers) absent at the last edition,
• University of Maryland, Baltimore (58 papers) published 1 paper at the last edition, 3 less than at the previous edition,
• University of California, San Francisco (58 papers) published 4 papers at the last edition the same number as 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, 4.76% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 11.67% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.17% of all publications and 59.17% 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.

Career Opportunities in Digital Imaging

While expanding your knowledge in the field of digital imaging, it might also be of interest to explore potential career paths. There are varied options to consider, from working in academia to research institutions or even industry. Specifically, teaching professions might be an interesting route to consider.

For instance, many academic institutions always need educators who are well-versed in the various topics covered in this journal, such as artificial intelligence and medical imaging. Becoming a high school teacher can be a rewarding career option, nurturing students' interest in science and technology and inspiring the next generation of digital imaging experts.

If you happen to be interested in this career path and are located in Mississippi, you can find more information about the necessary qualifications, required education level, and expected salary range at how much does a high school history teacher make in Mississippi. This guide will provide you with detailed insights into becoming a high school history teacher in Mississippi and help you embark on an exciting, fulfilling career.

On another note, the skills and knowledge acquired in these fields also open doors for careers in many other sectors and industries. This makes the study of digital imaging not only exciting from a scientific perspective but also as a stepping stone for career advancement.