Research Papers: Imaging

Taking advantage of hyperspectral imaging classification of urinary stones against conventional infrared spectroscopy

[+] Author Affiliations
Francisco Blanco

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

Felipe Lumbreras

Universitat Autònoma de Barcelona, Computer Vision Center & Department of Computer Science, 08193 Bellaterra, Spain

Joan Serrat

Universitat Autònoma de Barcelona, Computer Vision Center & Department of Computer Science, 08193 Bellaterra, Spain

Roswitha Siener

University Stone Centre, Department of Urology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany

Silvia Serranti

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

Giuseppe Bonifazi

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

Montserrat López-Mesas

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

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

J. Biomed. Opt. 19(12), 126004 (Dec 05, 2014). doi:10.1117/1.JBO.19.12.126004
History: Received June 24, 2014; Accepted October 24, 2014
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Abstract.  The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15μm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Francisco Blanco ; Felipe Lumbreras ; Joan Serrat ; Roswitha Siener ; Silvia Serranti, et al.
"Taking advantage of hyperspectral imaging classification of urinary stones against conventional infrared spectroscopy", J. Biomed. Opt. 19(12), 126004 (Dec 05, 2014). ; http://dx.doi.org/10.1117/1.JBO.19.12.126004


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