An efficient algorithm for human cell detection in electron microscope images based on cluster analysis and vector quantization techniques

Automatic detection of human cell is one of the most common investigation methods that may be used as part of a computer aided medical decision making system. In this paper we present an efficient algorithm, based on the cluster analysis and the vector quantization techniques for human cell image detection. First, we perform the edge detection methods to specify the desired region of any object in image and then apply vector quantization technique to cluster the property approximation of human cells. Our proposed algorithm is applied on two sample datasets from our research laboratory and also Imamreza laboratory in Mashhad which contain 196 number of normal electron microscope images. Experimental results show that this model is both accurate and fast with a detection rate of around 86.69 percent. Our proposed method does not require any under segmentation.