Hyperspectral imaging based method for fast characterization of
kidney stone types

J 2012

Hyperspectral imaging based method for fast characterization of kidney stone types

BLANCO, Francisco, Montserrat LOPEZ-MESAS, Silvia SERRANTI, Giuseppe BONIFAZI, Josef HAVEL et. al.

Základní údaje

Originální název

Hyperspectral imaging based method for fast characterization of kidney stone types

Autoři

BLANCO, Francisco, Montserrat LOPEZ-MESAS, Silvia SERRANTI, Giuseppe BONIFAZI, Josef HAVEL (203 Česká republika, domácí) a Manuel VALIENTE

Vydání

Journal of Biomedical Optics, BELLINGHAM, SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2012, 1083-3668

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10608 Biochemistry and molecular biology

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Full Text

Impakt faktor

Impact factor: 2.881

Kód RIV

RIV/00216224:14310/12:00107018

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1117/1.JBO.17.7.076027

UT WoS

000307989500054

Klíčová slova anglicky

kidney stone; hyperspectral imaging; artificial neural networks; renal calculi characterization

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 30. 4. 2020 13:00, Mgr. Michal Petr

Anotace

V originále

The formation of kidney stones is a common and highly studied disease, which causes intense pain and presents a high recidivism. In order to find the causes of this problem, the characterization of the main compounds is of great importance. In this sense, the analysis of the composition and structure of the stone can give key information about the urine parameters during the crystal growth. But the usual methods employed are slow, analyst dependent and the information obtained is poor. In the present work, the near infrared (NIR)-hyperspectral imaging technique was used for the analysis of 215 samples of kidney stones, including the main types usually found and their mixtures. The NIR reflectance spectra of the analyzed stones showed significant differences that were used for their classification. To do so, a method was created by the use of artificial neural networks, which showed a probability higher than 90% for right classification of the stones. The promising results, robust methodology, and the fast analytical process, without the need of an expert assistance, lead to an easy implementation at the clinical laboratories, offering the urologist a rapid diagnosis that shall contribute to minimize urolithiasis recidivism.

Návaznosti

ED2.1.00/03.0086, projekt VaV

Název: Regionální VaV centrum pro nízkonákladové plazmové a nanotechnologické povrchové úpravy

GA202/07/1669, projekt VaV

Název: Depozice termomechanicky stabilních nanostrukturovaných diamantu-podobných tenkých vrstev ve dvojfrekvenčních kapacitních výbojích

Investor: Grantová agentura ČR, Depozice termomechanicky stabilních nanostrukturovaných diamantu-podobných tenkých vrstev ve dvojfrekvenčních kapacitních výbojích

Citovat

BLANCO, Francisco, Montserrat LOPEZ-MESAS, Silvia SERRANTI, Giuseppe BONIFAZI, Josef HAVEL a Manuel VALIENTE. Hyperspectral imaging based method for fast characterization of kidney stone types. Journal of Biomedical Optics. BELLINGHAM: SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2012, roč. 17, č. 7, s. 1-13. ISSN 1083-3668. Dostupné z: https://dx.doi.org/10.1117/1.JBO.17.7.076027.

@article{1593418,
   author = {Blanco, Francisco and LopezandMesas, Montserrat and Serranti, Silvia and Bonifazi, Giuseppe and Havel, Josef and Valiente, Manuel},
   article_location = {BELLINGHAM},
   article_number = {7},
   doi = {http://dx.doi.org/10.1117/1.JBO.17.7.076027},
   keywords = {kidney stone; hyperspectral imaging; artificial neural networks; renal calculi characterization},
   language = {eng},
   issn = {1083-3668},
   journal = {Journal of Biomedical Optics},
   title = {Hyperspectral imaging based method for fast characterization of kidney stone types},
   url = {https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-17/issue-7/076027/Hyperspectral-imaging-based-method-for-fast-characterization-of-kidney-stone/10.1117/1.JBO.17.7.076027.full?SSO=1},
   volume = {17},
   year = {2012}
}

TY  - JOUR
ID  - 1593418
AU  - Blanco, Francisco - Lopez-Mesas, Montserrat - Serranti, Silvia - Bonifazi, Giuseppe - Havel, Josef - Valiente, Manuel
PY  - 2012
TI  - Hyperspectral imaging based method for fast characterization of kidney stone types
JF  - Journal of Biomedical Optics
VL  - 17
IS  - 7
SP  - 1-13
EP  - 1-13
PB  - SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
SN  - 10833668
KW  - kidney stone
KW  - hyperspectral imaging
KW  - artificial neural networks
KW  - renal calculi characterization
UR  - https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-17/issue-7/076027/Hyperspectral-imaging-based-method-for-fast-characterization-of-kidney-stone/10.1117/1.JBO.17.7.076027.full?SSO=1
L2  - https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-17/issue-7/076027/Hyperspectral-imaging-based-method-for-fast-characterization-of-kidney-stone/10.1117/1.JBO.17.7.076027.full?SSO=1
N2  - The formation of kidney stones is a common and highly studied disease, which causes intense pain and presents a high recidivism. In order to find the causes of this problem, the characterization of the main compounds is of great importance. In this sense, the analysis of the composition and structure of the stone can give key information about the urine parameters during the crystal growth. But the usual methods employed are slow, analyst dependent and the information obtained is poor. In the present work, the near infrared (NIR)-hyperspectral imaging technique was used for the analysis of 215 samples of kidney stones, including the main types usually found and their mixtures. The NIR reflectance spectra of the analyzed stones showed significant differences that were used for their classification. To do so, a method was created by the use of artificial neural networks, which showed a probability higher than 90% for right classification of the stones. The promising results, robust methodology, and the fast analytical process, without the need of an expert assistance, lead to an easy implementation at the clinical laboratories, offering the urologist a rapid diagnosis that shall contribute to minimize urolithiasis recidivism.
ER  -

BLANCO, Francisco, Montserrat LOPEZ-MESAS, Silvia SERRANTI, Giuseppe BONIFAZI, Josef HAVEL a Manuel VALIENTE. Hyperspectral imaging based method for fast characterization of kidney stone types. \textit{Journal of Biomedical Optics}. BELLINGHAM: SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2012, roč.~17, č.~7, s.~1-13. ISSN~1083-3668. Dostupné z: https://dx.doi.org/10.1117/1.JBO.17.7.076027.

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