K-means cluster algorithm based on color image enhancement for cell segmentation

Color cell image recognition and segmentation are two important issues in the field of biomedical cell morphology. The conventional segmentation method of color cell images based on k-means cluster is unreliable, since the color information from every category is similar. This paper presents a new method about cell segmentation by k-means cluster based on color image enhancement. Firstly, the cumulative distributions of the R, G, and B component gray value are calculated to find the mean value in the distribution. Secondly, the enhanced images are divided into three categories as masking images by k-means clustering algorithm in Ycbcr color space. And then, the binary images are de-noised via the morphological processing. Finally, the leukocytes and erythrocytes are segmented. The experimental results based on image enhancement mechanism by k-means clustering showed that the algorithm has a good discriminating and segmenting effect and while maintaining critical information color image.