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An Intensity Threshold based Image Segmentation of Malaria Infected Cells

机译：基于疟疾感染细胞的强度阈值基于图像分割

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Malaria is a perilous disease in charge for around 400 to 1000 deaths annually in India. The conventional technique to diagnose malaria is through microscopy. It takes few hours by an expert to examine and diagnose malarial parasites in the blood smear. The diagnosis report may vary when the blood smears are analyzed by different experts. In proposed work, an image processing based robust algorithm is designed to diagnose malarial parasites with minimal intervention of an expert. Initially, the images are enhanced by using green channel and histogram equalization, and the background subtraction is performed to get the clear vision of the region of interest. After preprocessing, a median filter is employed to eliminate the noise from the images. Then Otsu's method for segmentation is implemented on the filtered images. The database from world health organization is used in this research. The experiments give encouraging results and an accuracy up to 93%.
机译：疟疾是印度每年约为400至1000人死亡的危险疾病。诊断疟疾的常规技术是通过显微镜检查。专家需要几个小时，以检查和诊断血液涂片中的疟疾寄生虫。当不同专家分析血液涂片时，诊断报告可能会有所不同。在提出的工作中，基于图像处理的鲁棒算法旨在诊断具有专家的最小干预的疟疾寄生虫。最初，通过使用绿色通道和直方图均衡来增强图像，并且执行背景减法以获得感兴趣区域的清晰视觉。在预处理之后，采用中值滤波器来消除来自图像的噪声。然后，OTSU用于分割的方法在过滤的图像上实现。来自世界卫生组织的数据库在本研究中使用。实验给出令人鼓舞的结果，准确性高达93％。

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