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Vessel segmentation using multiscale vessel enhancement and a region based level set model.

Jinzhu Yang1, Chunhui Lou1, Jie Fu2

  • 1Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education, Northeastern University, Shenyang, Liaoning 110169, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110169, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|August 29, 2020
PubMed
Summary

This study introduces an effective method for medical vessel segmentation, improving contrast and using an improved level set model for accurate results. The approach achieves comparable segmentation accuracy to existing methods on retinal datasets.

Keywords:
Level setMulti-scaleVessel enhancementVessel segmentation

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Computer Vision

Background:

  • Vessel segmentation in medical images is challenging due to variations in vessel thickness and low contrast.
  • Accurate segmentation is crucial for diagnosing and monitoring various medical conditions.

Purpose of the Study:

  • To propose an effective method for enhancing contrast and segmenting blood vessels in medical images.
  • To evaluate the proposed method's performance on retinal vessel datasets and compare it with existing techniques.

Main Methods:

  • Image preprocessing including grayscale conversion and intensity expansion using mirroring.
  • An improved multi-scale enhancement method inspired by Frangi filtering.
  • Segmentation using an improved level set model applied to enhanced and original grayscale images.

Main Results:

  • The method demonstrated effective contrast enhancement between blood vessels and other image components.
  • Evaluated on DRIVE and STARE datasets, the proposed method achieved segmentation accuracy comparable to leading existing methods.
  • The study also discussed the method's applicability to segmenting other types of vessels.

Conclusions:

  • The proposed method offers an effective solution for challenging vessel segmentation tasks in medical imaging.
  • Its comparable performance highlights its potential as a reliable tool for clinical applications.
  • Further research could explore its efficacy across a broader range of medical imaging modalities.