Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Liu, Chanjuan, van Netten, Jaap J., Klein, Marvin E., Van Baal, Jeff G., Bus, Sicco A., & van Der Heijden, Ferdi (2013) Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot. Journal of Biomedical Optics, 18(12), -126004.

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Abstract

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

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4 citations in Scopus
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3 citations in Web of Science®

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ID Code: 93265
Item Type: Journal Article
Refereed: Yes
Keywords: Diabetic foot ulcer, Feature selection, Filter selection paper, Hyperspectral imaging, Skin spectrum
DOI: 10.1117/1.JBO.18.12.126004
ISSN: 1560-2281
Subjects: Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000)
Divisions: Current > Schools > School of Clinical Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Copyright Owner: Copyright 2013 SPIE
Deposited On: 26 Feb 2016 01:58
Last Modified: 04 Mar 2016 01:57

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