To improve the correct recognition rate of white blood cells images

the effective methods of image segmentation and feature extraction are studied in this article. Because of the existence of grains in some type of white blood cells (granulocyte)

the result of image segmentation is seriously affected. Integrating spatial information and kernel function into the fuzzy C-means clustering FCM algorithm

this paper proposes an improved FCM algorithm. Applying this new algorithm to image segmentation and taking the measure of mathematic morphology to process segmented image

the study gets a good segmentation effect and solves the problem of cytoplasm-nucleus of granulocyte segmentation. As for the feature extraction of cells

by fuzzification of the threshold parameter in local binary pattern(LBP)

the texture feature extraction method based on local fussy pattern (LFP)is proposed. The employment of the methods above in image segmentation and texture extraction supports vector machine as the classifier and tests the classification of 100 CellAtlas's white blood cells images. The results indicate that the correct recognition rate is up to 93%.