Biological object recognition in mu-radiography images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064211%3A_____%2F15%3A%230000457" target="_blank" >RIV/00064211:_____/15:#0000457 - isvavai.cz</a>
Alternative codes found
RIV/60076658:12310/15:43889008 RIV/00216208:11110/15:10296034
Result on the web
<a href="http://iopscience.iop.org/1748-0221/10/03/C03023" target="_blank" >http://iopscience.iop.org/1748-0221/10/03/C03023</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1748-0221/10/03/C03023" target="_blank" >10.1088/1748-0221/10/03/C03023</a>
Alternative languages
Result language
angličtina
Original language name
Biological object recognition in mu-radiography images
Original language description
This study presents an applicability of real-time microradiography to biological objects, namely to horse chestnut leafminer, Cameraria ohridella (Insecta: Lepidoptera, Gracillariidae) and following image processing focusing on image segmentation and object recognition. The microradiography of insects (such as horse chestnut leafminer) provides a non-invasive imaging that leaves the organisms alive. The imaging requires a high spatial resolution (micrometer scale) radiographic system. Our radiographic system consists of a micro-focus X-ray tube and two types of detectors. The first is a charge integrating detector (Hamamatsu flat panel), the second is a pixel semiconductor detector (Medipix2 detector). The latter allows detection of single quantum photon of ionizing radiation. We obtained numerous horse chestnuts leafminer pupae in several microradiography images easy recognizable in automatic mode using the image processing methods. We implemented an algorithm that is able to count a number of dead and alive pupae in images. The algorithm was based on two methods: 1) noise reduction using mathematical morphology filters, 2) Canny edge detection. The accuracy of the algorithm is higher for the Medipix2 (average recall for detection of alive pupae = 0 : 99, average recall for detection of dead pupae = 0 : 83), than for the flat panel (average recall for detection of alive pupae = 0 : 99, average recall for detection of dead pupae = 0 : 77). Therefore, we conclude that Medipix2 has lower noise and better displays contours (edges) of biological objects. Our method allows automatic selection and calculation of dead and alive chestnut leafminer pupae. It leads to faster monitoring of the population of one of the world's important insect pest.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BO - Biophysics
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
JOURNAL OF INSTRUMENTATION
ISSN
1748-0221
e-ISSN
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Volume of the periodical
10
Issue of the periodical within the volume
03
Country of publishing house
GB - UNITED KINGDOM
Number of pages
10
Pages from-to
nestrankovano
UT code for WoS article
000357944500023
EID of the result in the Scopus database
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