Abstract: To be able to quickly and accurately detect the morphology, proliferation, and differentiation of human cells, and evaluate the health of patients, an efficient adaptive cell image segmentation method based on the minimum operation of the color model is proposed. First, extract the S-component image with obvious characteristics in the HSV channel of the image, and use morphological reconstruction, H-minima technology, and image enhancement technology to perform gradient correction on the S-component image; after the gradient of the image is corrected, use the watershed algorithm to perform Segmentation: After the segmented image is obtained, the segmentation result is merged according to the gray level consistency of the original image to obtain the background, cell, and nucleus. Finally, post-morphological processing is used to remove the false noise in the merged result and flatten the edge of the region. The results of the segmentation test on cell images show that the accuracy of cell and cell nucleus segmentation is 0.9788 and 0.9677, respectively. The Dice coefficients are 0.9388 and 0.9370, which realizes the accurate segmentation of medical micro cell images.

Key words: Watershed, Morphological reconstruction, H-minima, Regional merger, Cell Image