Improving Magnetic Resonance Imaging Standardization of Human Brain using Several Statistical Methods-based Data

Document Type : Original Article

Authors

Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

In this paper, standardization of human brain MRI images is performed by applying several statistical methods The goal will be more clearly displayed after the standardization phase by removing the blurry and black noise spots, the border of the tumor areas and the different parts of the brain and cerebral fluid, which can be divided into three sections: white, gray and cerebral fluid. When a standard protocol is available, hybridization-based standardization and segmentation methods are one of the most appropriate tools for medical image segmentation. This method, despite its high accuracy, is time consuming and lengthy due to the high volume of computing. Methods based on statistical concepts are fashion, median, and mean, respectively, which are simple and explicit processing to apply to existing data from human brain MRI images. Boundary, decimal, and percentile methods are also used with simple concepts to extract milestones from existing data. To evaluate the performance of this paper, the scheme is first systematically simulated in MATLAB software, and then, for the area of comparison and comparison with the pre-standardization images, a software term Uvneti 8 was used to extract the gray layer. The results of the processing of this paper are visualized from standard images and statistical analysis of error and coefficient of variation are obtained and finally the most efficient method is extracted.

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References

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Advanced Signal Processing
Volume 3, Issue 2 - Serial Number 4
December 2019
Pages 119-124

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