Skin cells segmentation algorithm based on spectral angle and distance score

In the diagnosis of skin diseases by analyzing histopathological images of skin sections, the automated segmentation of cells in the epidermis area is an important step. Light microscopy based traditional methods usually cannot generate satisfying segmentation results due to complicated skin structures and limited information of this kind of image. In this study, we use a molecular hyperspectral imaging system to observe skin sections and propose a spectral based algorithm to segment epithelial cells. Unlike pixel-wise segmentation methods, the proposed algorithm considers both the spectral angle and the distance score between the test and the reference spectrum for segmentation. The experimental results indicate that the proposed algorithm performs better than the K-Means, fuzzy C-means, and spectral angle mapper algorithms because it can identify pixels with similar spectral angle but a different spectral distance.