The feasibility of smartphones for in vivo skin autofluorescence imaging has been investigated. Filtered autofluorescence images from the same tissue area were periodically captured by a smartphone RGB camera with subsequent detection of fluorescence intensity decreasing at each image pixel for further imaging the planar distribution of those values. The proposed methodology was tested clinically with 13 basal cell carcinoma and 1 atypical nevus. Several clinical cases and potential future applications of the smartphone-based technique are discussed.

Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.

A clinical trial on the autofluorescence imaging of skin lesions comprising 16 dermatologically confirmed pigmented nevi, 15 seborrheic keratosis, 2 dysplastic nevi, histologically confirmed 17 basal cell carcinomas and 1 melanoma was performed. The autofluorescence spatial properties of the skin lesions were acquired by smartphone RGB camera under 405 nm LED excitation. The diagnostic criterion is based on the calculation of the mean autofluorescence intensity of the examined lesion in the spectral range of 515 nm-700 nm. The proposed methodology is able to differentiate seborrheic keratosis from basal cell carcinoma, pigmented nevi and melanoma. The sensitivity and specificity of the proposed method was estimated as being close to 100%. The proposed methodology and potential clinical applications are discussed in this article.

In this study, an optical contactless laser speckle imaging technique for the early identification of bacterial colony-forming units was tested. The aim of this work is to compare the laser speckle imaging method for the early assessment of microbial activity with standard visual inspection under white light illumination. In presented research, the growth of Vibrio natriegens bacterial colonies on the solid medium was observed and analyzed. Both – visual examination under white light illumination and laser speckle correlation analysis were performed. Based on various experiments and comparisons with the theoretical Gompertz model, colony radius growth curves were obtained. It was shown that the Gompertz model can be used to describe both types of analysis. A comparison of the two methods shows that laser speckle contrast imaging, combined with signal processing, can detect colony growth earlier than standard CFU counting method under white light illumination.

Melanoma is a melanocytic tumor that is responsible for the most skin cancer-related deaths. By contrast, seborrheic keratosis (SK) is a very common benign lesion with a clinical picture that may resemble melanoma. We used a multispectral imaging device to distinguish these two entities, with the use of autofluorescence imaging with 405 nm and diffuse reflectance imaging with 525 and 660 narrow-band LED illumination. We analyzed intensity descriptors of the acquired images. These included ratios of intensity values of different channels, standard deviation and minimum/maximum values of intensity of the lesions. The pattern of the lesions was also assessed with the use of particle analysis. We found significantly higher intensity values in SKs compared with melanoma, especially with the use of the autofluorescence channel. Moreover, we found a significantly higher number of particles with high fluorescence in SKs. We created a parameter, the SK index, using these values to differentiate melanoma from SK with a sensitivity of 91.9% and specificity of 57.0%. In conclusion, this imaging technique is potentially applicable to distinguish melanoma from SK based on the analysis of various quantitative parameters. For this application, multispectral imaging could be used as a screening tool by general physicians and non-experts in the everyday practice.