Red blood cell segmentation by thresholding and Canny detector

Abstract

A computer visualization system that can detect, count and classify cells in bioimages is needed, and the segmentation of cells plays a vital role in this type of system. In the medical field, blood cell testing is among the most important types of clinical examinations. Manual blood cell examination methods using microscopic devices are more time consuming than automatic methods and require radiologists with more technical skills. However, the development of an effective and fully automatic segmentation process for RBCs (red blood cells) remains challenging. This paper proposes an automatic segmentation algorithm for RBCs that automatically computes the threshold image using boundary-based methods after enhancing the local and global details of the output using morphological operations to segment red blood cells in bioimages. The proposed segmentation method exhibited an average accuracy rate of 87.9% in a public human red blood cell dataset. Furthermore, a comparison of this method with gold segmentation and two other methods typically used for this purpose demonstrated that the proposed method was highly robust and outperformed the other methods.