Assessment of immunohistochemically stained slides is often a crucial diagnostic step in clinical practice. However, as this assessment is generally performed visually by pathologists it can suffer from signi?cant inter-observer variability. The introduction of whole slide scanners facilitates automated analysis of immunohistochemical slides. Color deconvolution (CD) is one of the most popular ?rst steps in quantifying stain density in histopathological images. However, color deconvolution requires stain color vectors for accurate unmixing. Often it is assumed that these stain vectors are static. In practice, however, they are influenced by many factors. This can cause inferior CD unmixing and thus typically results in poor quanti?cation. Some automated methods exist for color stain vector estimation, but most depend on a signi?cant amount of each stain to be present in the whole slide images. In this paper we propose a method for automatically ?nding stain color vectors and unmixing IHC stained whole slide images, even when some stains are sparsely expressed. We collected 16 tonsil slides and stained them for different periods of time with hematoxylin and a DAB-colored proliferation marker Ki67. RGB pixels of WSI images were converted to the hue saturation density (HSD) color domain and subsequently K-means clustering was used to separate stains and calculate the stain color vectors for each slide. Our results show that staining time affects the stain vectors and that calculating a unique stain vector for each slide results in better unmixing results than using a standard stain vector.