Abstract: A saturation clustering-based method for positioning bone marrow white blood cells: first, pre-processing a bone marrow white blood cell image to eliminate partial noise points and simultaneously smooth the image; using K-means clustering to cluster saturation channels of the bone marrow white blood cell image, and select the type of the white blood cells according to a decision tree algorithm; next, eliminating irrelevant areas in a binary image of the white blood cells by means of a morphology processing algorithm, and simultaneously filling in point holes in the white blood cells; and finally, positioning the white blood cells. The present method is simple and effective, and is suitable for a wide range of applications compared to existing threshold-based algorithms, while rendering a final result more accurate by integrating the decision tree algorithm.

Abstract: A saturation clustering-based method for positioning bone marrow white blood cells: first, pre-processing a bone marrow white blood cell image to eliminate partial noise points and simultaneously smooth the image; using K-means clustering to cluster saturation channels of the bone marrow white blood cell image, and select the type of the white blood cells according to a decision tree algorithm; next, eliminating irrelevant areas in a binary image of the white blood cells by means of a morphology processing algorithm, and simultaneously filling in point holes in the white blood cells; and finally, positioning the white blood cells. The present method is simple and effective, and is suitable for a wide range of applications compared to existing threshold-based algorithms, while rendering a final result more accurate by integrating the decision tree algorithm.