Wijesinghe Chinthalanka B, Wickramarachchi Dilshan N, Kalupahana Iyani N, De Seram Lokesha R, Silva Indira D, Nanayakkara Nuwan D
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1816-1819. doi: 10.1109/EMBC44109.2020.9175961.
The measure of White Blood Cells (WBC) in the blood is an important indicator of pathological conditions. Computer vision based methods for differential counting of WBC are increasing due to their advantages over traditional methods. However, most of these methods are proposed for single WBC images which are pre-processed, and do not generalize for raw microscopic images with multiple WBC. Moreover, they do not have the capability to detect the absence of WBC in the images. This paper proposes an image processing algorithm based on K-Means clustering to detect the presence of WBC in raw microscopic images and to localize them, and a VGG-16 classifier to classify those cells with a classification accuracy of 95.89%.
血液中白细胞（WBC）的测量是病理状况的重要指标。基于计算机视觉的白细胞分类计数方法因其相对于传统方法的优势而不断增加。然而，这些方法大多是针对经过预处理的单个白细胞图像提出的，不适用于具有多个白细胞的原始显微图像。此外，它们没有能力检测图像中白细胞的缺失情况。本文提出了一种基于K均值聚类的图像处理算法，用于检测原始显微图像中白细胞的存在并对其进行定位，还提出了一种VGG - 16分类器，用于对这些细胞进行分类，分类准确率为95.89%。
