Acta Scientific Applied Physics (ASAP)

Review Article Volume 2 Issue 2

Application of MATLAB and 3D SLICER in Medical Imaging - Preliminary Discussions Application of Medical Advance Mathematics

Jian Gao*

International Education School, China Medical University, P. R. China

*Corresponding Author: Jian Gao, International Education School, China Medical University, P. R. China.

Received: November 29, 2021; Published: January 24, 2022

Abstract

Medical advance mathematics is a compulsory course for all medical students when entered medical high education system. This paper represented studying experiences of this course, meanwhile, its applications MATLAB and 3D Slicer into medical imaging as well as preliminary discussions about functional dependences of correlations. This paper also demonstrates the discussion of the combinational features in open source software 3D Slicer and well-known scientific software MATLAB from engineer and radiologist point of view. Development of usage in 3D Slicer in medical research, as well as the MATLAB, demanding in different fields. Not only the medical applications, such as autism, multiple sclerosis, systemic lupus erythematosus, prostate cancer, schizophrenia, orthopedic biomechanics, COPD, cardiovascular disease and neurosurgery, but also 3D printing, artificial intelligence. This discussion represents the experience and review comments when using these two pieces of software simultaneously, from engineer and radiologist point of view. Finally, it concludes the future studies and the possibilities of transformations in other applications and version of software.

Keywords: Algorithms; Bidirectional Interface; Data Structure; Multi Organ; Multi - Modality Imaging

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Citation

Citation: Jian Gao. “Application of MATLAB and 3D SLICER in Medical Imaging - Preliminary Discussions Application of Medical Advance Mathematics". Acta Scientific Applied Physics 2.2 (2022): 19-22.

Copyright

Copyright: © 2022 Jian Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate32%
Acceptance to publication20-30 days
Impact Factor1.014

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