A semiautomatic procedure for edge detection of in vivo pulmonary microvessels and interstitial space
(1997) In Microcirculation 4(4). p.455-468- 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 interstitium thickness. RESULTS: Microvessel diameters and perivascular interstitium thickness were calculated for precapillary arteriolar branching (40 to 140 microns) and... (More)
- 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 interstitium thickness. RESULTS: Microvessel diameters and perivascular interstitium thickness were calculated for precapillary arteriolar branching (40 to 140 microns) 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: magnitude of sigma Y2 - sigma X2 < or = (mu X - mu Y)2, where mu X, mu Y, sigma X2, sigma Y2 are the statistical moments of the two regions X and Y. Moreover, when the regions have similar means, the above conditions is not met, but the edge between them can be detected by the maximum of the moving variance error. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1111674
- author
- Crisafulli, Beatrice ; Boschetti, Federica ; Venturoli, Daniele LU ; Del Fabbro, Massimo and Miserocchi, Giuseppe
- publishing date
- 1997
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- morphometry, image analysis, image segmentation, pulmonary microcirculation, pulmonary interstitium, perivascular interstitium, lung surface
- in
- Microcirculation
- volume
- 4
- issue
- 4
- pages
- 455 - 468
- publisher
- Taylor & Francis
- external identifiers
-
- pmid:9431513
- scopus:0031309482
- ISSN
- 1549-8719
- DOI
- 10.3109/10739689709146809
- language
- English
- LU publication?
- no
- id
- 47e6589b-ee94-43b7-af22-a1215baa47ba (old id 1111674)
- date added to LUP
- 2016-04-01 12:24:12
- date last changed
- 2022-01-27 03:16:23
@article{47e6589b-ee94-43b7-af22-a1215baa47ba,
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 interstitium thickness. RESULTS: Microvessel diameters and perivascular interstitium thickness were calculated for precapillary arteriolar branching (40 to 140 microns) 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: magnitude of sigma Y2 - sigma X2 < or = (mu X - mu Y)2, where mu X, mu Y, sigma X2, sigma Y2 are the statistical moments of the two regions X and Y. Moreover, when the regions have similar means, the above conditions is not met, but the edge between them can be detected by the maximum of the moving variance error.}},
author = {{Crisafulli, Beatrice and Boschetti, Federica and Venturoli, Daniele and Del Fabbro, Massimo and Miserocchi, Giuseppe}},
issn = {{1549-8719}},
keywords = {{morphometry; image analysis; image segmentation; pulmonary microcirculation; pulmonary interstitium; perivascular interstitium; lung surface}},
language = {{eng}},
number = {{4}},
pages = {{455--468}},
publisher = {{Taylor & Francis}},
series = {{Microcirculation}},
title = {{A semiautomatic procedure for edge detection of in vivo pulmonary microvessels and interstitial space}},
url = {{http://dx.doi.org/10.3109/10739689709146809}},
doi = {{10.3109/10739689709146809}},
volume = {{4}},
year = {{1997}},
}