Pan Chen,Yan Xiangguo,Zheng Chongxun,Liang Chengwen
(Key Laboratory of Biomedical Information Engineering of Education Ministry,Xi'an Jiaotong University,Xi'an 710049,China)
Abstract:A new method for segmentation of blood micrograph images is proposed which combines the meanshift algorithm with oneclass support vector machine(SVM) to extract white blood cells. The one-class SVM model is trained by the positive samples made up of some background and red blood cell pixels, then the white blood cell pixels are detected as outlier data by the model. The mean-shift procedures are used to find the clustering modes of positive samples in RGB space. By means of uniform sampling and color quantization, the size of training set can be limited within 1000,so the training can be completed near to real time.The experimental results on 200 blood images demonstrate the higher segmentation accuracy and efficiency of the new method than the traditional watershed algorithm, and it takes only a quarter of the later’s operating time. It brings robust performance to cope with the change of color results from varied preparation and illumination in image acquisition.
Keywords:color image segmentation;one-class support vector machine;mean-shift;blood cells