{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T11:58:06Z","timestamp":1791633486004,"version":"4.3.4"},"reference-count":34,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2014,11,10]],"date-time":"2014-11-10T00:00:00Z","timestamp":1415577600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100004331","name":"Johnson & Johnson","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100004331","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Skin Research and Technology"],"published-print":{"date-parts":[[2015,8]]},"abstract":"<jats:sec><jats:title>Background\/purpose<\/jats:title><jats:p>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\u2010pixel. However, breathing and involuntary motion of the patient causes significant misalignment. Alignment algorithms designed for <jats:italic>multimodal<\/jats:italic> and <jats:italic>time\u2010lapse<\/jats:italic> skin images can solve this problem. Sequences from multi\u2010modal imaging capture unique appearance features in each modality. Time\u2010lapse image sequences capture skin appearance change over time.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Multimodal skin images have been acquired under five different modalities: three in reflectance (visible, parallel\u2010polarized, and cross\u2010polarized) and two in fluorescence mode (<jats:styled-content style=\"fixed-case\">UVA<\/jats:styled-content> and blue light excitation). For time\u2010lapse imagery, 39 images of acne lesions over a 3\u2010month period have been collected. The method detects micro\u2010level features like pores, wrinkles, and other skin texture markings in the acquired images. Images are automatically registered to subpixel accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed registration approach precisely aligns multimodal and time\u2010lapse images. Subsurface recovery from multimodal images has misregistration artefacts that can be eliminated using this approach. Registered time\u2010lapse imaging captures the evolution of appearance of skin regions with time.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Misalignment in skin imaging has significant impact on any quantitative or qualitative image evaluation. Micro\u2010level features can be used to obtain highly accurate registration. Multimodal images can be organized with maximal overlap for successful registration. The resulting point\u2010to\u2010point alignment improves the quality of skin image analysis.<\/jats:p><\/jats:sec>","DOI":"10.1111\/srt.12195","type":"journal-article","created":{"date-parts":[[2014,11,10]],"date-time":"2014-11-10T05:38:19Z","timestamp":1415597899000},"page":"319-326","source":"Crossref","is-referenced-by-count":2,"title":["Multimodal and time\u2010lapse skin registration"],"prefix":"10.1111","volume":"21","author":[{"given":"S.","family":"Madan","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. 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