A novel skin pigment separation method based on sub-block selection and local clustering

Skin pigment separation is a key task in the fields of medical aesthetics, clinical analysis, and dermatological diagnosis. Existing pigment separation methods suffer from the problem of unclear separation between melanin and hemoglobin.

This study proposes a novel approach that combines a sub-block selection algorithm with local clustering for skin pigment separation. The sub-block selection algorithm sorts and filters sub-blocks based on the average pixel difference, reconstructing the input data to ensure accurate separation of melanin and hemoglobin. The local clustering method uses the Euclidean distance between sample points and their surrounding cluster centers to assign sample points to their respective clusters. A random sampling of sample points is performed from each cluster to ensure convergence of independent component analysis during the iteration process.

Experimental evaluations demonstrate that the proposed method achieves accuracy and precise separation in skin pigment separation. Furthermore, the average success convergence rate of this approach reaches 92%, surpassing the performance of existing methods.