基于 Hedge 算子的模糊散度测度的发展及其在外周血涂片显微镜图像中慢性髓系白血病细胞分割中的应用。
Development of hedge operator based fuzzy divergence measure and its application in segmentation of chronic myelogenous leukocytes from microscopic image of peripheral blood smear.
机构信息
School of Medical Science and Technology, I.I.T., Kharagpur, India.
School of Medical Science and Technology, I.I.T., Kharagpur, India.
出版信息
Micron. 2014 Feb;57:41-55. doi: 10.1016/j.micron.2013.10.008. Epub 2013 Oct 21.
This paper introduces a hedge operator based fuzzy divergence measure and its application in segmentation of leukocytes in case of chronic myelogenous leukemia using light microscopic images of peripheral blood smears. The concept of modified discrimination measure is applied to develop the measure of divergence based on Shannon exponential entropy and Yager's measure of entropy. These two measures of divergence are compared with the existing literatures and validated by ground truth images. Finally, it is found that hedge operator based divergence measure using Yager's entropy achieves better segmentation accuracy i.e., 98.29% for normal and 98.15% for chronic myelogenous leukocytes. Furthermore, Jaccard index has been performed to compare the segmented image with ground truth ones where it is found that that the proposed scheme leads to higher Jaccard index (0.39 for normal, 0.24 for chronic myelogenous leukemia).
本文介绍了一种基于 Hedge 算子的模糊离度测度及其在慢性髓系白血病外周血涂片光学显微镜图像白细胞分割中的应用。应用修正判别测度的概念，基于香农指数熵和 Yager 的熵测度来开发基于熵的离度测度。将这两种离度测度与现有文献进行了比较，并通过真实图像进行了验证。最后发现，基于 Yager 熵的 Hedge 算子离度测度可实现更好的分割精度，即正常细胞为 98.29%，慢性髓系白血病细胞为 98.15%。此外，还进行了 Jaccard 指数比较，以将分割图像与真实图像进行比较，结果表明，所提出的方案可获得更高的 Jaccard 指数（正常细胞为 0.39，慢性髓系白血病为 0.24）。
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