Medical Image Analysis

An official journal of the Society

Medical Image Analysis - ISSN 1361-8415
Source Normalized Impact per Paper (SNIP): 2.838 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 1.948 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 4.188 (2016) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
5 Year Impact Factor: 5.012 (2016) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2016 to the previous five years and divided by the source items published in the previous five years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
Volumes: Volumes 43-50
Issues: 8 issues
ISSN: 13618415

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Description

Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. The journal publishes the highest quality, original papers that contribute to the basic science of processing, analysing and utilizing medical and biological images for these purposes. The journal is interested in approaches that utilize biomedical image datasets at all spatial scales, ranging from molecular/cellular imaging to tissue/organ imaging. While not limited to these alone, the typical biomedical image datasets of interest include those acquired from:

  • Magnetic resonance
  • Ultrasound
  • Computed tomography
  • Nuclear medicine
  • X-ray
  • Optical and Confocal Microscopy
  • Video and range data images


The types of papers accepted include those that cover the development and implementation of algorithms and strategies based on the use of various models (geometrical, statistical, physical, functional, etc.) to solve the following types of problems, using biomedical image datasets: representation of pictorial data, visualization, feature extraction, segmentation, inter-study and inter-subject registration, longitudinal / temporal studies, image-guided surgery and intervention, texture, shape and motion measurements, spectral analysis, digital anatomical atlases, statistical shape analysis, computational anatomy (modelling normal anatomy and its variations), computational physiology (modelling organs and living systems for image analysis, simulation and training), virtual and augmented reality for therapy planning and guidance, telemedicine with medical images, telepresence in medicine, telesurgery and image-guided medical robots, etc.

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