RESUMO
BACKGROUND/PURPOSE: Computational skin analysis is revolutionizing modern dermatology. Patterns extracted from image sequences enable algorithmic evaluation. Stacking multiple images to analyze pattern variation implicitly assumes that the images are aligned per-pixel. However, breathing and involuntary motion of the patient causes significant misalignment. Alignment algorithms designed for multimodal and time-lapse skin images can solve this problem. Sequences from multi-modal imaging capture unique appearance features in each modality. Time-lapse image sequences capture skin appearance change over time. METHODS: Multimodal skin images have been acquired under five different modalities: three in reflectance (visible, parallel-polarized, and cross-polarized) and two in fluorescence mode (UVA and blue light excitation). For time-lapse imagery, 39 images of acne lesions over a 3-month period have been collected. The method detects micro-level features like pores, wrinkles, and other skin texture markings in the acquired images. Images are automatically registered to subpixel accuracy. RESULTS: The proposed registration approach precisely aligns multimodal and time-lapse images. Subsurface recovery from multimodal images has misregistration artefacts that can be eliminated using this approach. Registered time-lapse imaging captures the evolution of appearance of skin regions with time. CONCLUSION: Misalignment in skin imaging has significant impact on any quantitative or qualitative image evaluation. Micro-level features can be used to obtain highly accurate registration. Multimodal images can be organized with maximal overlap for successful registration. The resulting point-to-point alignment improves the quality of skin image analysis.