Research Papers: Imaging

Hyperspectral imaging based method for fast characterization of kidney stone types

[+] Author Affiliations
Francisco Blanco, Montserrat López-Mesas, Manuel Valiente

Universitat Autònoma de Barcelona, Centre Grup de Tècniques de Separació en Química (GTS), Unitat de Química Analítica, Departament de Química, 08193 Bellaterra, Spain

Silvia Serranti, Giuseppe Bonifazi

Sapienza–Università di Roma, Dipartimento di Ingegneria Chimica Materiali Ambiente, 00184 Roma, Italy

Josef Havel

Masaryk University, Department of Chemistry, Faculty of Science, Kamenice 5/A14, 625 00 Brno, Czech Republic

Masaryk University, Department of Physical Electronics, Faculty of Science, Kotlárská 2, 611 37 Brno, Czech Republic

Masaryk University, R&D center for low-cost plasma and nanotechnology surface modifications, Kotlárská 2, 611 37 Brno, Czech Republic

J. Biomed. Opt. 17(7), 076027 (Jul 25, 2012). doi:10.1117/1.JBO.17.7.076027
History: Received October 28, 2011; Revised May 30, 2012; Accepted June 26, 2012
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Abstract.  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.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Francisco Blanco ; Montserrat López-Mesas ; Silvia Serranti ; Giuseppe Bonifazi ; Josef Havel, et al.
"Hyperspectral imaging based method for fast characterization of kidney stone types", J. Biomed. Opt. 17(7), 076027 (Jul 25, 2012). ; http://dx.doi.org/10.1117/1.JBO.17.7.076027


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