Abstract
SIGNIFICANCE: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability. AIM: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach. The obtained hyperfocus images should significantly enhance the diagnostic performance of total body scanners in clinical settings. APPROACH: We employed focus stacking to merge multiple images, each with a limited depth of field, into a single hyperfocus image, ensuring every part of the skin is in clear focus. The method was implemented in the high-resolution imaging module using an electrically tunable liquid lens to quickly capture a series of differently focused images in vivo. Algorithms were developed for image alignment, focus measurement, and fusion, with the addition of deep learning-based super-resolution techniques to further enhance image quality. A classification model was trained to provide an artificial intelligence (AI)-based lesion classification. RESULTS: The developed optical imaging system successfully produced noncontact dermoscopic images with complete focus across all skin topographies. The hyperfocus images obtained demonstrated high resolution of 28 μm and captured focus stacks at 50 frames per second, ensuring rapid acquisition and patient comfort, however, with some variance in resolution of individual lesions compared with contact-based dermoscopy standards. CONCLUSIONS: The focus stacking-based approach for noncontact dermoscopy improves the quality of diagnostic images by ensuring an all-in-focus view of differently shaped skin lesions, essential for early melanoma detection. Although the approach marks a substantial improvement in noninvasive skin imaging, the use of super-resolution techniques requires careful consideration to avoid compromising the authenticity of the raw data. This work enables the usage of advanced imaging and AI techniques in total body scanners for early melanoma detection in clinical practice.