Unsupervised classification of cell images using pyramid node linking

A segmentation technique which combines two properties in an iterative and hierarchical manner to correctly segment and classify the given cell images is described. The technique is applied to digital images taken from microscope slides of cultured rat liver cells, and the goal is to classify these cells into one of three possible classes. The cells of the first class (I) are morphologically normal and stain the darkest. Those of the second class (II) are slightly damaged, showing both nuclear and cytoplasmic swelling with resultant lessening of staining affinity. The cells of the third class (III) are markedly damaged, as demonstrated by the presence of cytoplasmic vacuolization, or are completely disintegrated. Cells of the first class are classified by taking advantage of the staining affinity; the original gray-level image is segmented into four gray levels. The darkest is then classified as type I. Type III cells are classified by using high busyness as a characteristic; the standard deviation of the original image is segmented into four busyness levels. The highest level is classified as type III cells. Assuming that only the three cell types are present in any given image, the remaining nonbackground unclassified pixels are determined to belong to type II cells.