Artifacts and landmarks: pearls and pitfalls for in vivo reflectance confocal microscopy of the skin using the tissue-coupled device

Reflectance confocal microscopy (RCM) is a non-invasive imaging tool for cellular-level examination of skin lesions, typically from the epidermis to the superficial dermis. Clinical studies show RCM imaging is highly sensitive and specific in the diagnosis of skin diseases. RCM is disseminating from academic tertiary care centers with early adopter "experts" into diverse clinical settings, with image acquisition performed by technicians and image interpretation by physicians. In the hands of trained users, RCM serves an aid to accurately diagnose and monitor skin tumors and inflammatory processes. However, exogenous and endogenous artifacts introduced during imaging can obscure RCM images, limiting or prohibiting interpretation. Herein we review the types of artifacts that may occur and techniques for mitigating them during image acquisition, to assist technicians with qualitative image assessment and provide physicians guidance on identifying artifacts that may confound interpretation. Finally, we discuss normal skin "landmarks" and how they can (i) obscure images, (ii) be exploited for additional diagnostic information, and (iii) simulate pathological structures. A deeper understanding of the principles and methods behind RCM imaging and the varying appearance of normal skin structures in the acquired images aids technicians in capturing higher quality image sets and enables physicians to increase interpretation accuracy

Filtered Variation method for denoising and sparse signal processing

We propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that are widely used in signal processing. We mathematically define the FV problem, and solve it using alternating projections in space and transform domains. We provide a globally convergent algorithm based on the projections onto convex sets approach. We apply to our algorithm to real denoising problems and compare it with the total variation recovery

Computational methods in skin confocal microscopy

Interest in clinical use of reflectance confocal microscopy (RCM) has recently been increased, with its successful demonstration of effectiveness in diagnostic and surgical guidance. However, this initial success is currently limited to experienced clinicians, who adopted RCM imaging at early stages and have been using it for research and clinical screening purposes for a while. On the other hand, a majority of the new cohort of users is rather interested in using RCM mostly in clinical practice, where time pressure and strict regulations exist. The current system is manual and depends highly on the experience of the users. This typically leads to variability both in RCM image acquisition and analysis. Therefore, standardized protocols for rapid and consistent imaging as well as standardized image analysis tools to guide patient care must be developed. Beyond the medical needs for such procedures that are described in the other chapters, in this chapter, we will further look into the technical side of the problem and demonstrate how the clinical needs can be dealt with using computer-aided tools, such as computer vision and machine learning algorithms