An Automatic Detection for Avian Blood Cell based on Adaptive Thresholding Algorithm

Abstract

The chicken industry ranks tenth in the world in term of output in the world meat market. Red blood cell (RBC) count is one of the basic health screening protocol required in an exported meat industry. However, the human’s automated blood analyzer cannot be applied to avian RBC, because the shape of avian blood (ellipse) is different from the mammal one (circle). In this paper, we propose 2-stage thresholding technique for automatic avian RBC counting. First, the blood smear slide is binarized into RBC and non-RBC area by applying Otsu thresholding. Then, image morphology and Otsu thresholding are reapplied to detect the blood nucleus. After that, the connected component analysis is applied to count the number of RBC. The experiment demonstrated that the proposed technique was simple and provided the count with error rate (2.23%) much less than the clinically acceptable value (5%).