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A vessel segmentation method for serialized cerebralvascular DSA images based on spatial feature point set of

Bin Liu1, Qianfeng Jiang2, Wenpeng Liu2

  • 1Department of Digital Media Technology, Dalian University of Technology, Dalian 116620, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian 116620, China.

Computer Methods and Programs in Biomedicine
|June 2, 2018
PubMed

Insights

This study presents an automatic method to segment brain vascular tissue from Digital Subtraction Angiography (DSA) images, overcoming motion artifacts. The technique enhances diagnostic accuracy for cerebrovascular pathology, crucial for clinical interventions.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Radiology

Background:

  • Cerebrovascular diseases are a leading cause of mortality.
  • Accurate imaging of cerebral vasculature is vital for diagnosis.
  • Motion artifacts in Digital Subtraction Angiography (DSA) degrade image quality.

Purpose of the Study:

  • To develop an automatic and accurate segmentation method for extracting cerebral vascular regions from live DSA images.
  • To improve the quality of vascular subtraction images by addressing motion artifacts.

Main Methods:

  • Coarse registration of live and mask images.
  • Scale-Invariant Feature Transform (SIFT) algorithm for feature point detection.
  • Spatial model and contextual information strategy to eliminate erroneous feature points.
  • Dynamic thresholding and region growing for final vessel segmentation.

Main Results:

  • The proposed method effectively segments cerebral vascular regions.
  • Experimental results demonstrate satisfactory quality of the segmented images.
  • The technique successfully utilizes contextual information from adjacent subtraction images.

Conclusions:

  • The developed method provides accurate vessel image data for clinical operations.
  • This technique supports interventional therapy based on DSA.
  • Improved DSA image quality aids in better diagnosis and treatment of cerebrovascular diseases.

Keywords:
Cerebrovascular image segmentationContextual informationDigital Subtraction AngiographyRotating coordinate system

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