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White blood cell (WBC) detection is one of the most basic and key steps in the automatic WBC recognition system. Its accuracy and stability greatly affect the recognition accuracy of the whole system. This paper presents a novel method for WBC detection based on boundary support vectors (BSVs). Firstly, v-Support Vector Regression (v-SVR) is introduced. Then sparse BSVs are obtained while fitting the 1D histogram by v -SVR. Next so-needed threshold value is directly sifted from these limited support vectors. Finally the entire connective WBC regions are segmented from the original cell image. The proposed method successfully works for WBC detection, and effectively reduces the influence brought by illumination and staining. It also has the advantages, such as high computing efficiency and easy parameter setting. Experimental results demonstrate its good performances.