The Classification Research On Microscopic Leucocyte Image

Intelligent microscopic image Recognition of Blood corpuscle is a main problem. Microscopic image Segmentation and recognition of leucocyte is of real significance. The basic task is to check and calculate the main parameters of leucocytes, such as the quantity, the comparative ratio, the modality, and etc., for diagnosing the presence, type and severity of diseases. This automatic process could not only help improve the efficiency of hemanalysis work and lighten the labor burden but also enhance the precision of examinations.In this thesis, an automatic recognition system of leucocyte is presented using a PC, a microscope and a color CCD camera based on other researcher's study with the technology in image.In order to recognize the leucocyte, we must take the follow steps: capture of microscopic leucocyte image, pretreatment and segmentation of image, characters selecting and distill and classified recognition of image.Firstly, according to the characters of leucocyte, we introduce an equipment to capture microscopic leucocyte images with Wright's staining. Secondly, we analyze the feature of leucocyte images and take pretreatments of smoothness and sharpness in order to get the image without noise, and then apply an algorithm of segmentation for nucleus extraction with saturation based on the HSI color space. The leucocyte image including cytoplasm can be extracted approximately in complete with the location of center of gravity for nucleus. Finally, we use the eigenvector with shape feature for nucleus and color feature for cytoplasm to recognize the leucocyte. The shape feature is Zernike moments and HU moments which are invariable for image translation, rotation and zoom. The color feature is the average of cytoplasm color. In pattern recognition module, we selcet BP neural network method to classify the eigenvector. According to the existing example, the weight and limen in BP neural network can be decided. At last, we use a lot of samplers to test recognition system, and get a satisfied result. This research also do plenty of work on software design, we adopt orient-object method to design system and complete all coding in VC++6.0.