White blood cell (WBC) image detection plays an important role in automatic morphological systems since it can simplify and facilitate WBC segmentation and classification procedures. However, existing WBC detection methods mainly rely on the location of the nucleus, which is found difficult to achieve accurate detection results. This paper proposes a novel WBC detection algorithm through sliding windows with varying sizes to traverse the image for candidates. Three cues are explored to measure the candidates, and a combined cue is used as a single output to distinguish positives from negatives. The \(\epsilon \)-support vector regression is employed to determine the detection window from the candidates. In this paper, two applications of the proposed WBC detection approach are carried out, including an adaptive thresholding algorithm based on WBC detection for nucleus segmentation from images and target detection to lessen the users’ interaction for automatic cytoplasm segmentation.

中文翻译：

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基于\（\ epsilon \）-SVR的检测方法对白细胞图像进行有效分割

白细胞（WBC）图像检测在自动形态学系统中起着重要作用，因为它可以简化和促进WBC分割和分类程序。然而，现有的WBC检测方法主要依赖于核的位置，发现难以获得准确的检测结果。本文提出了一种新颖的WBC检测算法，该算法可以通过滑动具有不同大小的窗口来遍历候选图像。探索了三种提示来衡量候选者，并使用组合提示作为单个输出来区分阳性和阴性。\（\ epsilon \）-支持向量回归被用来从候选者确定检测窗口。本文对提出的WBC检测方法进行了两次应用，