Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Aims & Scope

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users.

The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science
• Embase

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Engineering and Medicine, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Computer Science, Engineering and Medicine, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 2,630.00 USD. Learn more.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Microscopy cell counting and detection with fully convolutional regression networks

  Citation: 270

  Authors: Weidi, J. Alison, Andrew

• Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

  Citation: 159

  Authors: Mingchen, Ulas, Le, Aaron, Mario, Hoo-Chang, Holger, Georgios Z., Adrien, Ronald M., Ziyue, Daniel J.

• Automatic machine learning model for enhanced partition and identification of breast disorders in breast MRI scan

  Citation: 158

  Authors: Harendra, Arun Kumar, Jayant, Mohd Asif, Saurav, T.

• A CNN-based methodology for breast cancer diagnosis using thermal images

  Citation: 121

  Authors: J., Z., K., S., N.

• Deep similarity learning for multimodal medical images

  Citation: 99

  Authors: Xi, Li, Yefeng

• Machine Learning Model and Cuckoo Search in a modular system to identify Alzheimer’s disease from MRI scan images

  Citation: 65

  Authors: Saravanan, Saravanakumar

• An anatomical region-based statistical shape model of the human femur

  Citation: 62

  Authors: Ju, Duane, Jacqui, C. David L., Poul M.F.

• Efficient Framework for Brain Tumour Classification using Hierarchical Deep Learning Neural Network Classifier

  Citation: 58

  Authors: Francis H, Salini, P., Venu Kadur

• A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images

  Citation: 57

  Authors: Anas, Guangmin, Sarah, Azhar, Jahanzaib

• A data-driven investigation and estimation of optimal topologies under variable loading configurations

  Citation: 52

  Authors: Erva, Rusheng, Levent Burak