USF-LVHN SELECT

Improving dermal level images from reflectance confocal microscopy using wavelet-based transformations and adaptive histogram equalization.

Publication/Presentation Date

3-1-2022

Abstract

OBJECTIVES: Reflectance confocal microscopy (RCM) generates scalar image data from serial depths in the skin, allowing in vivo examination of cellular features. The maximum imaging depth of RCM is approximately 250 µm, to the papillary dermis, or upper reticular dermis. Frequently, important diagnostic features are present in the dermis, hence improved visualization of deeper levels is advantageous.

METHODS: Low contrast and noise in dermal images were improved by employing a combination of wavelet-based transformations and contrast-limited adaptive histogram equalization.

RESULTS: Preserved details, noise reduction, increased contrast, and feature enhancement were observed in the resulting processed images.

CONCLUSIONS: Complex and combined wavelet-based enhancement approaches for dermal level images yielded reconstructions of higher quality than less sophisticated histogram-based strategies. Image optimization may improve the diagnostic accuracy of RCM, especially for entities with dermal findings.

Volume

54

Issue

3

First Page

384

Last Page

391

ISSN

1096-9101

Disciplines

Medical Education | Medicine and Health Sciences

PubMedID

34633691

Department(s)

USF-LVHN SELECT Program, USF-LVHN SELECT Program Students

Document Type

Article

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