Automated classification of healthy and keloidal collagen patterns based on processing of SHG images of human skin.

All-optical microspectroscopic and tomographic tools have a great potential for the clinical investigation of human skin and skin diseases. However, automated optical tomography or even microscopy generate immense data sets. Therefore, in order to implement such diagnostic tools into the medical practice in both hospitals and private practice, there is a need for automated data handling and image analysis ideally implementing automized scores to judge the physiological state of a tissue section. In this contribution, the potential of an image processing algorithm for the automated classification of skin into normal or keloid based on second-harmonic generation (SHG) microscopic images is demonstrated. Such SHG data is routinely recorded within a multimodal imaging approach. 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.