## Abstract

Objective: To develop an algorithm to detect the edges between lung tissue, perivascular interstitium, and microvessel using digital processing of in vivo microscopic images of lung surface. Methods: A numerical technique was developed to identify three different regions (namely, pulmonary microvessel, perivascular interstitium, and lung tissue) based on their corresponding gray level distributions. We present a theoretical demonstration of the method and a semiautomatic procedure that, once the edges are detected, determines microvascular diameters and perivascular interstitum thickness. Results: Microvessel diameters and perivascular interstitium thickness were calculated for precapillary arteriolar branching (40 to 140 μm) and saved in an ASCII file. Conclusions: We proved that the maximum value of the moving variance is useful to detect the edge between two adjacent regions whose gray level distributions satisfy the condition: |σ_{y}^{2} - σ_{x}^{2}| ≤ (μ_{x} - μ_{y})^{2}, where μ_{x}, μ_{y} σ_{x}^{2}, σ_{y}^{2} are the statistical moments of the two regions X and Y. Moreover, when the regions have similar means, the above condition is not met, but the edge between them can be detected by the maximum of the moving variance error.

Original language | English |
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Pages (from-to) | 455-468 |

Number of pages | 14 |

Journal | Microcirculation |

Volume | 4 |

Issue number | 4 |

Publication status | Published - Dec 1997 |

## Keywords

- Image analysis
- Image segmentation
- Lung surface
- Morphometry
- Perivascular interstitium
- Pulmonary interstitium
- Pulmonary microcireulntion

## ASJC Scopus subject areas

- Genetics
- Physiology
- Cardiology and Cardiovascular Medicine