Computerized Medical Imaging and Graphics

Aims & Scope

The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health.

Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health.

The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	4.9
2024	5.40

SJR (SCImago Journal Rank)

Year	Value
2024	1.270

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	100

Journal Rank

Year	Value
2024	3210

Journal Citation Indicator

Year	Value
2024	2305

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Abstracting & Indexing

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

• Scopus
• Google Scholar
• Web of Science
• Medline
• Embase

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Subjects & Keywords

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

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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.

• WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians

  Citation: 1477

  Authors: Jorge, F. Javier, Gloria, Debora, Cristina, Fernando

• Computer-aided diagnosis in medical imaging: Historical review, current status and future potential

  Citation: 1379

  Authors: Kunio

• Fuzzy c-means clustering with spatial information for image segmentation

  Citation: 1041

  Authors: Keh-Shih, Hong-Long, Sharon, Jay, Tzong-Jer

• Brain tumor classification for MR images using transfer learning and fine-tuning

  Citation: 634

  Authors: Zar Nawab Khan, Qinghua, Muhammad, Farman, Zakir, Saeed, Jianfeng

• A methodological approach to the classification of dermoscopy images

  Citation: 549

  Authors: M. Emre, Hassan A., Bakhtiyar, Hitoshi, Y. Alp, William V., Randy H.

• MedGAN: Medical image translation using GANs

  Citation: 470

  Authors: Karim, Chenming, Marc, Thomas, Tobias, Konstantin, Sergios, Bin

• Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

  Citation: 443

  Authors: Michael, Evis, Carola-Bibiane, Leonardo

• Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation

  Citation: 363

  Authors: Li, Chunming, Quansen, Deshen, Chiu-Yen

• Lesion border detection in dermoscopy images

  Citation: 333

  Authors: M.Emre, Hitoshi, Gerald, William V.

• Combo loss: Handling input and output imbalance in multi-organ segmentation

  Citation: 332

  Authors: Saeid Asgari, Yefeng, S., Bogdan, Puneet, Daguang, Dorin, Ghassan

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