Background
In vivo confocal Raman spectroscopy (CRS) revealed a clear correlation of age and dermal water content, indicating increasing water content of the dermis with increasing age. This enhancement of water has been interpreted as an age‐dependent depletion, of proteins, mainly of collagen. Chronical sun exposure is known to destroy the collagen network of the skin, which leads to the signs of photoaging as the formation of wrinkles. Noninvasive in vivo measuring techniques for photoaging are limited. Therefore, sensitive techniques to quantify even mild degrees of photoaging in a clinical setting are of high interest. We used CRS to measure the water content in human dermis in vivo, assuming that additionally to the increase of water content in intrinsic aging, photoaging would lead to further collagen depletion and an additional increase in water content of the dermis.
Materials and Methods
A panel of 24 female subjects of different age‐groups and scores of photoaging was recruited. A ranking of high resolution dorsal forearm photographs was used to classify the degree of photoaging with high precision. After that, CRS water content and collagen measurements were performed in the photoexposed dorsal as well as the photoprotected volar dermis of the subjects.
Results
A positive correlation of water content in the dermis with age could be confirmed (r = .550). Further, a positive correlation between water content of dorsal dermis and photoaging ranks was observed (Pearson's r = .417).
Conclusion
Assessment of water content in the dermis with confocal Raman spectroscopy was found to be a promising method to measure the degree of photoaging in human subjects in vivo.
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Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. It is evaluated on the task of segmenting tibial contours in knee radiographs. Results are presented using various features, distance weighted K−nearest neighbours and differing eigenspace shape constraints.
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