Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.

Neurofibromatosis type 1 (NF1) is a rare disease, affecting around 1 in 3500 individuals in the general population. The rarity of the disease contributes to the scarcity of the available diagnostic and therapeutic approaches. Multispectral imaging is a non-invasive imaging method that shows promise in the diagnosis of various skin diseases. The device utilized for the present study consisted of four sets of narrow-band LEDs, including 526 nm, 663 nm, and 964 nm for diffuse reflectance imaging and 405 nm LEDs, filtered through a 515 nm long-pass filter, for autofluorescence imaging. RGB images were captured using a CMOS camera inside of the device. This paper presents the results of this multispectral skin imaging approach to distinguish the lesions in patients with NF1 from other more common benign skin lesions. The results show that the method provides a potential novel approach to distinguish NF1 lesions from other benign skin lesions.

This work will describe the challenges involved in setting up automatic processing for a large differentiated data set. In this study, a multispectral (skin diffuse reflection images using 526nm (green), 663nm (red), and 964nm (infrared) illumination and autofluorescence (AF) image using 405 nm excitation) data set with 756 lesions (3024 images) was processed. Previously, using MATLAB software, finding markers, correctly segmenting images with dark edges and image alignment were the main causes of the problems in automatic data processing. To improve automatic processing and eliminate the use of licensed software, the latter was substituted with the open source Python environment. For more precise segmentation of skin markers and skin lesions, as well for image alignment, the processing of artificial neural networks was utilized. The resulting processing method solves most of the issues of the MATLAB script. However, for even more accurate results, it is necessary to provide more accurate ground-truth segmentation masks and generate more input data to increase the training image database by using data augmentation.

In this work post-operative skin cancer scar evaluation with LED screening device has been described. The wavelength used for inducing autofluorescence (AF) of chromophores in the skin is 405nm. The green channel of the captured images is the best to calculate AF intensity ratio from the scar and the surrounding skin of 10 patients with healthy healing and scars with cancer recurrence. This non-invasive multispectral screening method can help dermato-oncologist to make a decision on evaluating if the scar is healing correctly and evaluate any pigmentation that could be suspected as a recurrent cancer.

The laser speckle imaging system developed in this study monitored Pleurotus ostreatus growth in real-time. This non-invasive method reliably mapped microbial activity with low data volume and fast processing, accelerating microbiological experiments and enhancing high-throughput screening applications.