Automatic working area localization in blood smear microscopic images using machine learning algorithms

Microscopic examination of a properly prepared blood smear is valuable in complete blood count (CBC) and differential blood count (DBC). A hematopathologist may spend enormous time manually inspecting the good working area (GWA) of the blood smear under a light microscope system to perform CBC or DBC. In this paper we focus on automatic localization of the GWA by classifying microscopic images of blood smears using different machine learning algorithms into three areas: Clumped, Good, and Sparse. The features used are the statistical and texture features. This approach yields a good localization of GWA in images acquired by a low cost light microscope system, scanned under magnifying power of x100 oil-immersed objective. Our experiment using images with resolution (3488×2616 pixels) of Giemsa-stained blood smears shows that the proposed method has an accuracy of 82% for the localizing the GWA and 79.73% for all areas in a validation set of 301 images.