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Multiple Hidden Markov Model for Pathological Vessel Segmentation.

Xin Hu1, Deqiong Ding2, Dianhui Chu1

  • 1School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China.

Biomed Research International
|January 16, 2019
PubMed
Summary
This summary is machine-generated.

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This study introduces a new method using multiple Hidden Markov Models (HMMs) for accurate vessel segmentation, especially in pathological cases. The approach improves boundary delineation for complex vessel structures.

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Image Analysis

Background:

  • Accurate vessel boundary delineation is crucial for medical diagnosis but is hindered by pathologies causing obscure boundaries and vessel-like structures.
  • Existing segmentation methods struggle with the complexity introduced by pathological conditions in vascular imaging.

Purpose of the Study:

  • To develop a novel segmentation method that accurately delineates vessel boundaries, particularly in the presence of pathologies.
  • To address the limitations of current techniques in segmenting complex and obscured vascular structures.

Main Methods:

  • A novel segmentation method employing multiple Hidden Markov Models (HMMs) was developed.
  • The method utilizes a vessel axis plus cross-section model to constrain the segmentation process, ensuring physiological accuracy and computational efficiency.

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  • It leverages redundant information from multiple vessel-specific features and the statistical properties of HMMs to handle complex pathological cases.

    • Main Results:

    • The method achieved a 91% overlap ratio on synthetic complex-structured datasets.
    • Validation on real clinical data, specifically pathological abdominal arteries, demonstrated promising performance.
    • The proposed method outperformed two state-of-the-art methods on both synthetic and real clinical datasets.

    Conclusions:

    • The novel HMM-based segmentation method effectively delineates pathological vessels with high accuracy.
    • The approach offers a physiologically accurate and computationally effective solution for challenging vascular segmentation tasks.
    • This method shows significant potential for improving the analysis of vascular pathologies in medical imaging.