The Application Of Fuzzy C-Means Algorithm In The Segmentation Of Milk Somatic Cell Color Image

The accurate segmentation of milk somatic cells in microscope images may contribute to development of a successful system which automaticly analyze,detect and count cells in microscope images. It is important to improve the milk quality detection and diagnose bovine mastitis. Therefore, the thesis mainly concentrates on researching the segmentation the segmentation of milk somatic cell color images.Variance analysis and contrast method are applied to select color space. The results showed that RGB is more suitable for milk somatic cell image in RGB, HSI and Lab. Three color components R, G and B are analysed by variance analysis, The results showed that R , G color values is used in image segmentation.Because the image segmentation methods of 3-D color spaces consume large amount of computation and have slow speed. They do not suit to real-time applications. For those reasons, an approach for color image segmentation, called fuzzy c-means algorithm and images fusion are presented. For RGB color images, the band subsets are chosen as RB, R and G. segmentation results are then combined to obtain a final result using image fusion technology. The results are compared to segmentation in 3-D and segmentation in 2-D and 1D fusion, The results showed that this method correct rate is high and less time-consuming. In practical application, milk somatic cell color images were segmented by the method using the R and G color values is used. Milk somatic cell image is realized under C++ Builder.