Brain MRA 3D Skeleton Extraction Based on Normal Plane Centroid Algorithm

Guoying Feng; Jie Zhu; Jun Li

doi:10.4108/eetpht.9.4450

Authors

Guoying Feng College of Basic Medicine Binzhou Medical University, Yantai 264003, Shandong, China
Jie Zhu Center of Network Information Binzhou Medical University, Yantai 264003, Shandong, ChinaCenter of Network Information Binzhou Medical University, Yantai 264003, Shandong, China
Jun Li Department of Medical Imaging Yantai Affiliated Hospital Binzhou Medical University, Yantai 264100, Shandong, China

DOI:

https://doi.org/10.4108/eetpht.9.4450

Keywords:

MRA, average plane centroid algorithm, 3D reconstruction, skeleton extraction

Abstract

INTRODUCTION: Analysis of magnetic resonance angiography image data is crucial for early detection and prevention of stroke patients. Extracting the 3D Skeleton of cerebral vessels is the focus and difficulty of analysis.

OBJECTIVES: The objective is to remove other tissue components from the vascular tissue portion of the image with minimal loss by reading MRA image data and performing processing processes such as grayscale normalization, interpolation, breakpoint detection and repair, and image segmentation to facilitate 3D reconstruction of cerebral blood vessels and the reconstructed vascular tissues make extraction of the Skeleton easier.

METHODS: Considering that most of the existing techniques for extracting the 3D vascular Skeleton are corrosion algorithms, machine learning algorithms require high hardware resources, a large number of learning and test cases, and the accuracy needs to be confirmed, an average plane center of mass computation method is proposed, which improves the average plane algorithm by combining the standard plane algorithm and the center of mass algorithm.

RESULTS: Intersection points and skeleton breakpoints on the Skeleton are selected as critical points and manually labeled for experimental verification, and the algorithm has higher efficiency and accuracy than other algorithms in directly extracting the 3D Skeleton of blood vessels.

CONCLUSION: The method has low hardware requirements, accurate and reliable image data, can be automatically modeled and calculated by Python program, and meets the needs of clinical applications under information technology conditions.

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