In this paper, we propose a novel and efficient method for detecting and quantifying red and white blood cells from microscopic blood images. Laboratory tests that use a cell counter or a flow cytometer can perform a complete blood count (CBC) rapidly. Nonetheless, a manual blood smear inspection is still needed, both to have a human check on the counter results and to monitor patients under therapy. Moreover, it allows for describing the cells' appearance as well as any abnormalities. However, manual analysis is lengthy and repetitive, and its result can be subjective and error-prone. In contrast, by using image processing techniques, the proposed system is entirely automated. The main effort is devoted to both achieving high accuracy and finding a way to overcome the typical differences in the condition of blood smear images that computer-aided methods encounter. It is based on the Edge Boxes method, which is considered a state-of-art region proposal approach. By incorporating knowledge-based constraints into the detection process using Edge Boxes, we can find cell proposals rapidly and efficiently. We tested the proposed approach on the Acute Lymphoblastic Leukaemia Image Database (ALL-IDB), a well-known public dataset proposed for leukaemia detection, and the Malaria Parasite Image Database (MP-IDB), a recently proposed dataset for malaria detection. Experimental results were excellent in both cases, outperforming the state-of-the-art on ALL-IDB and creating a strong baseline on MP-IDB, demonstrating that the proposed method can work well on different datasets and different types of images.
Keywords: Cell counting; Cell detection; Edge boxes; Peripheral blood cell images; Region proposal.
Copyright © 2019 Elsevier Ltd. All rights reserved.