Multimodal and time-lapse skin registration.

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.

Assessing facial wrinkles: automatic detection and quantification.

BACKGROUND: As people mature, their skin gradually presents lines, wrinkles, and folds that become more pronounced with time. Skin wrinkles are perceived as important cues in communicating information about the age of the person. Nowadays, documenting the facial appearance through imaging is prevalent in skin research, therefore detection and quantitative assessment of the degree of facial wrinkling can be a useful tool for establishing an objective baseline and for assessing benefits to facial appearance due to various dermatological treatments. However, few image-based algorithms for computationally assessing facial wrinkles are present in the literature, and those that exist have limited reliability. METHODS: In this work, an algorithm for automatic detection of facial wrinkles is developed, based on estimating the orientation and the frequency of elongated spatial features, captured via digital image filtering. RESULTS: The algorithm is tested against one set of clinically validated 11-point wrinkle scales present on the face. The algorithm is employed for assessing the presence of forehead furrows on a set of 100 clinically graded facial images. The proposed computational assessment correlates well with the corresponding clinical scores. CONCLUSION: We find that the results are in better agreement with clinical scoring when the wrinkle depth information, approximated via filter responses, is combined with the wrinkle length information as opposed to the case when the two measures are considered separately.