Research Papers: Imaging

Photoacoustic discrimination of vascular and pigmented lesions using classical and Bayesian methods

[+] Author Affiliations
Jennifer A. Swearingen

University of Missouri, Department of Dermatology, One Hospital Drive, MA111, Columbia, Missouri 65211

Scott H. Holan

University of Missouri, Department of Statistics, 134F Middlebush Hall, Columbia, Missouri 65211

Mary M. Feldman

ProPath Laboratories, 8267 Elmbrook Drive, Suite 100, Dallas, Texas 75247

John A. Viator

University of Missouri, Department of Dermatology and Department of Biological Engineering240C Christopher S. Bond Life Sciences Center, 1201 East Rollins Road, Columbia, Missouri, 65211

J. Biomed. Opt. 15(1), 016019 (March 01, 2010). doi:10.1117/1.3316297
History: Received March 18, 2009; Revised December 15, 2009; Accepted December 15, 2009; Published March 01, 2010; Online March 01, 2010
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Discrimination of pigmented and vascular lesions in skin can be difficult due to factors such as size, subungual location, and the nature of lesions containing both melanin and vascularity. Misdiagnosis may lead to precancerous or cancerous lesions not receiving proper medical care. To aid in the rapid and accurate diagnosis of such pathologies, we develop a photoacoustic system to determine the nature of skin lesions in vivo. By irradiating skin with two laser wavelengths, 422 and 530nm, we induce photoacoustic responses, and the relative response at these two wavelengths indicates whether the lesion is pigmented or vascular. This response is due to the distinct absorption spectrum of melanin and hemoglobin. In particular, pigmented lesions have ratios of photoacoustic amplitudes of approximately 1.4 to 1 at the two wavelengths, while vascular lesions have ratios of about 4.0 to 1. Furthermore, we consider two statistical methods for conducting classification of lesions: standard multivariate analysis classification techniques and a Bayesian-model-based approach. We study 15 human subjects with eight vascular and seven pigmented lesions. Using the classical method, we achieve a perfect classification rate, while the Bayesian approach has an error rate of 20%.

Figures in this Article
SR445, Stanford Research Systems, Sunnyvale, California

Citation

Jennifer A. Swearingen ; Scott H. Holan ; Mary M. Feldman and John A. Viator
"Photoacoustic discrimination of vascular and pigmented lesions using classical and Bayesian methods", J. Biomed. Opt. 15(1), 016019 (March 01, 2010). ; http://dx.doi.org/10.1117/1.3316297


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