Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Top Research Topics at Computer methods in biomechanics and biomedical engineering. Imaging & visualization?

The foci of Computer methods in biomechanics and biomedical engineering. Imaging & visualization are Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Radiology. Deep learning, Image segmentation, Convolutional neural network, Image processing and Image (mathematics) are all subfields of Artificial intelligence research that were featured in it. Topics in Computer vision were tackled in line with various other fields like Magnetic resonance imaging and Medical imaging.

It is focused mainly on Pattern recognition, particularly Support vector machine. The majority of Segmentation studies presented zero in on Scale-space segmentation.

• Artificial intelligence (46.31%)
• Computer vision (30.87%)
• Pattern recognition (15.66%)

What are the most cited papers published in the journal?

• Microscopy cell counting and detection with fully convolutional regression networks (227 citations)
• Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. (151 citations)
• Deep similarity learning for multimodal medical images (142 citations)

Research areas of the most cited articles at Computer methods in biomechanics and biomedical engineering. Imaging & visualization:

The journal articles investigate studies in Artificial intelligence, Computer vision, Convolutional neural network, Deep learning and Segmentation. The works on Artificial intelligence tackled in the journal publications bring together disciplines like Machine learning and Pattern recognition. The studies on Computer vision discussed at the most cited publications can also contribute to research in the domains of Text mining and Discriminative model.

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

• Artificial intelligence
• Surgery
• Internal medicine

The previous edition focused in particular on these issues:

The journal is organized to address concerns in the fields of Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Visualization. It focuses on Artificial intelligence but the discussions also offer insight into other areas such as Genetic algorithm and Machine learning. The work on Computer vision tackled in Computer methods in biomechanics and biomedical engineering. Imaging & visualization brings together disciplines like Virtual reality and Position (vector).

It dives deep in exploring the relationship between the study of Virtual reality and Surface (mathematics). The Segmentation study tackling the subject of Image segmentation is the focus of the journal. Discussions in the journal are anchored in the subject of Convolutional neural network and the similar topic of Net (mathematics).

The most cited articles from the last journal are:

• A CNN-based methodology for breast cancer diagnosis using thermal images (14 citations)
• Automatic segmentation of brain tumour in MR images using an enhanced deep learning approach (8 citations)
• Classification techniques in breast cancer diagnosis: A systematic literature review (6 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 Computer methods in biomechanics and biomedical engineering. Imaging & visualization (based on the number of publications) are:

• Sidney Fels (10 papers) absent at the last edition,
• João Manuel R. S. Tavares (10 papers) absent at the last edition,
• Ian Stavness (8 papers) absent at the last edition,
• Durval C. Costa (7 papers) absent at the last edition,
• Amine Ben Slama (6 papers) published 3 papers at the last edition the same number as 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 Computer methods in biomechanics and biomedical engineering. Imaging & visualization (based on the number of publications) are:

• University of British Columbia (14 papers) absent at the last edition,
• University of Porto (14 papers) absent at the last edition,
• Sapienza University of Rome (11 papers) published 1 paper at the last edition the same number as at the previous edition,
• Johns Hopkins University (9 papers) published 4 papers at the last edition,
• Tunis University (9 papers) published 5 papers at the last edition, 1 more 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, 4.88% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.67% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.41% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.51% of all publications and 56.41% 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.

Further Applications and Careers in Biomechanics and Biomedical Engineering

There are numerous practical applications of computer methods in biomechanics and biomedical engineering. Many people may wonder what type of careers they can pursue in this field. One such career pathway is to become a teacher, specifically a preschool teacher. Preschool teachers play a crucial role in early childhood development, and their effectiveness can greatly be enhanced through the knowledge and application of biomechanics and biomedical engineering concepts. For example, understanding a child's physical development and growth patterns can help in designing age-appropriate learning activities and classroom setups. Additionally, concepts of biomedical engineering can also be used to invent or improve assistive tools for children with special needs. Moreover, incorporating technology and artificial intelligence elements can potentially make learning more engaging and interactive for young learners. Preschool teachers who are knowledgeable in these fields could help mold a future generation of innovators and tech enthusiasts. For anyone interested in pursuing this unconventional combination of preschool teaching and biomechanics and biomedical engineering, Louisiana could be a promising starting point. You may be wondering: "how do you become a preschool teacher in Louisiana?" But before diving into the exact steps, one must know that this blend of career path is both challenging and rewarding. Not only does it open doors to opportunities in academia and the children's development sector, but it also entails a chance to make a real and tangible difference in young children lives.