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

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.

Analysis of "Fingerprint" Characteristics of Wireless Channels of Sensors in Mobile Communications

“…For the noisy signal, the most widespread processing method is the threshold wavelet noise reduction method used in seismic data analysis, (13) medical field, (14,15) mechanical fault diagnosis, (16) and other aspects of noise reduction applications, which show significant results. This method mainly uses the Mallat algorithm for the wavelet analysis of the signal, using the characteristics of small noise wavelet coefficients, by setting a suitable threshold.…”