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Second-harmonic generation (SHG) microscopy has a great potential for the clinical investigation of human skin and skin diseases, especially in combination with others nonlinear optical modalities. However, such multimodal approaches generate immense datasets, which requires automated data handling. In this contribution, the potential of an SHG-image processing algorithm for the automated classification of skin into normal or keloid is demonstrated. The classification of the tissue implemented in the algorithm employs the geometrical features of collagen patterns that differ depending on the constitution, i.e., physiological status of the skin.
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