http://www.cnr.it/ontology/cnr/individuo/prodotto/ID14026
Automatic Detection and Surface Measurements of Micronucleus by a Computer Vision Approach (Articolo in rivista)
- Type
- Label
- Automatic Detection and Surface Measurements of Micronucleus by a Computer Vision Approach (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/TIM.2010.2049184 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Ceccarelli M; Speranza A; Grimaldi D; Lamonaca F (literal)
- Pagina inizio
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- http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5508418 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- Scopu (literal)
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- M. Ceccarelli is with the Research Centre on Software Technologies
(RCOST), University of Sannio, 82100 Benevento, Italy and also with the
Bioinformatics core, Biogem s.c.a.r.l., 83031 Ariano Irpino (AV), Italy (e-mail:
michele.ceccarelli@unisannio.it).
A. Speranza is with the Biostructure and Bioimaging Institute, National
Research Council, 80131 Napoli, Italy and also with the Bioinformatics
core, Biogem s.c.a.r.l., 83031 Ariano Irpino (AV), Italy (e-mail: antonio.
speranza@ibb.cnr.it).
D. Grimaldi and F. Lamonaca are with the Department of Electronic,
Computers, and System Science, University of Calabria, 87036 Rende (CS),
Italy (e-mail: dir.deis@unical.it). (literal)
- Titolo
- Automatic Detection and Surface Measurements of Micronucleus by a Computer Vision Approach (literal)
- Abstract
- This paper reports a novel method for nucleus and
micronucleus segmentation. These biological structures are useful
to biologists for relieving structural chromosome aberration. The
adopted method consists of a pipeline of advanced computer vision
algorithms, some of them were specifically tailored for the current
segmentation problem. Starting from the weak hypotheses on
size, shape, and color of micronucleus, it is possible to efficiently
segment and measure the image features of interest by a com-
puter vision approach. We report experimental results with a new
flow cytometer architecture specifically developed to recognize
and measure micronucleus of human lymphocyte. The robustness
of the algorithm with respect to various kinds of noise is also
reported. (literal)
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