0895-6111
Published by: Elsevier
https://www.journals.elsevier.com/computerized-medical-imaging-and-graphics
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 174 | 163 | 201 | 29 |
| Medicine | 1614 | 31 | 33 | 16 |
The journal focuses on Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Radiology. Image processing, Image segmentation, Deep learning, Convolutional neural network and Visualization are among the areas of Artificial intelligence tackled. The Computer vision study featured in Computerized Medical Imaging and Graphics draws connections with the study of Medical imaging.
Presentations on Segmentation include those discussing Scale-space segmentation and Segmentation-based object categorization. Computerized Medical Imaging and Graphics facilitates discussions on Pattern recognition that incorporate concepts from other fields like Artificial neural network and Feature (computer vision). The study on Radiology presented in it intersects with subjects under the field of Pathology.
The Magnetic resonance imaging study featured in it draws parallels with the field of Nuclear medicine. The journal connects the study in Iterative reconstruction with the closely related area of Algorithm.
The journal articles primarily focus on research topics in Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Image processing. The published articles with studies in Computer vision featured incorporate elements of Visualization and Computer-aided diagnosis. In addition to Segmentation research, the journal articles aim to explore topics under Tomography, Magnetic resonance imaging, Radiology and Robustness (computer science).
The journal generally zeroes in on subjects such as Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network. Topics in Artificial intelligence explored in it were investigated in conjunction with research in Machine learning and Computer vision. Pattern recognition research presented in it encompasses a variety of subjects, including Image quality, Image (mathematics), Channel (digital image) and Receiver operating characteristic.
The journal explores topics in Deep learning which can be helpful for research in disciplines like Optical coherence tomography, Classifier (linguistics), Image processing, Preprocessor and Feature extraction. Thresholding, Magnetic resonance imaging, Radiology, Boundary (topology) and Convolution are some topics wherein Segmentation research discussed in it have an impact. Convolutional neural network research presented in the journal encompasses a variety of subjects, including Image resolution, Computational complexity theory, Contextual image classification, Region of interest and Voxel.
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.
The top authors publishing in Computerized Medical Imaging and Graphics (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in Computerized Medical Imaging and Graphics (based on the number of publications) are:
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.
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, 3.82% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 23.02% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.56% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.11% of all publications and 60.32% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
It's also important to discuss the career prospects in the field of Computerized Medical Imaging and Graphics. With the ongoing advancements and breakthroughs, professionals and scholars in this domain have myriad opportunities to explore. From academic institutions to healthcare industries, they can carve out their career in several arenas where their expertise can bring about a significant impact. Being a part of the Artificial Intelligence revolution in healthcare, they can work on building advanced Computer-Aided Diagnosis systems, improving overall patient care and diagnosis. Furthermore, the skills like Deep Learning, Image Processing, Segmentation, and Radiology are in high demand in diverse industries.
However, to build a successful career in this domain, one needs to attain a specialized education and undergo comprehensive training. For instance, imaging specialists like Radiologists or Nuclear Medicine Technologists require not only a bachelor's but also a master's degree for advanced roles. For those interested in combining their passion for AI with teaching, they can even consider becoming an educator in related fields. Here is a guide on how to become a preschool teacher in Oklahoma, which can give a basic idea of what it takes to venture into academia.
In conclusion, with appropriate skills and determination, building a promising career in Computerized Medical Imaging and Graphics is absolutely achievable. The possibilities are endless for those ready to explore and contribute to this exciting field.
Karim Armanious;Karim Armanious;Chenming Jiang;Marc Fischer;Thomas Küstner
(2020)Jose Dolz;Christian Desrosiers;Li Wang;Jing Yuan
(2020)Wei Shao;Yao Peng;Chen Zu;Mingliang Wang
(2020)Mingfeng Jiang;Minghao Zhi;Liying Wei;Xiaocheng Yang
(2021)Hisham Abdeltawab;Fahmi Khalifa;Fatma Taher;Norah Saleh Alghamdi
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