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Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Automatic lumen segmentation in IVOCT images using binary morphological reconstruction.

Matheus Cardoso Moraes1, Diego Armando Cardona Cardenas, Sérgio Shiguemi Furuie

  • 1Department of Telecommunication and Control, School of Engineering of the University of São Paulo, Av, Prof, Luciano Gualberto, Travessa 3, 158 - sala D2-06, São Paulo, SP CEP 05508-970, Brazil. matheuscardosomg@hotmail.com

Biomedical Engineering Online
|August 14, 2013
PubMed
Summary

This study introduces an automatic lumen segmentation method for Intravascular Optical Coherence Tomography (IVOCT) images. The novel approach achieves high accuracy, offering a valuable tool for improved cardiovascular diagnostics.

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Cardiovascular Research

Background:

  • Atherosclerosis is a leading cause of mortality and significant healthcare expenditure globally.
  • Intravascular Optical Coherence Tomography (IVOCT) provides high-resolution cross-sectional images of coronary arteries.
  • Accurate quantitative analysis of IVOCT images requires robust segmentation methods for improved diagnostics and interventions.

Purpose of the Study:

  • To develop and evaluate an automatic lumen segmentation technique for Intravascular Optical Coherence Tomography (IVOCT) images.
  • To enhance the diagnostic capabilities of IVOCT through precise lumen segmentation.
  • To provide a reliable and automated tool for analyzing complex vascular structures.

Main Methods:

  • A three-stage methodology involving preprocessing, feature extraction, and binary object construction.
  • Application of Wavelet Transform and an adapted Otsu threshold for effective tissue discrimination and binarization.
  • Utilization of Mathematical Morphology, specifically binary morphological reconstruction, to refine segmentation results and define the lumen boundary.

Main Results:

  • The automatic segmentation method demonstrated high accuracy on 290 challenging IVOCT images from human, pig, and rabbit arteries.
  • Achieved a True Positive rate of 99.29% ± 2.96% and a False Positive rate of 3.69% ± 2.88%.
  • Minimal segmentation errors were observed, with Max False Positive Distance of 0.1 mm ± 0.07 mm and Max False Negative Distance of 0.06 mm ± 0.1 mm.

Conclusions:

  • The proposed automatic lumen segmentation technique is robust and highly accurate for IVOCT image analysis.
  • Outperforms previously published segmentation methods in terms of accuracy and reliability.
  • Presents a fully automated and effective new tool for IVOCT image segmentation, aiding clinical applications.