Total white blood cell (WBC) count is usually performed in laboratory by using manual techniques and haemocytometer. Differential counting has also been done by stained blood smear and light microscopy. Present of nucleated erythrocytes and thrombocytes in avian blood prevents from automated method that have been developed for mammals. Also, manual counting is time-consuming and requires to an experienced technician. Therefore, this project was carried out to examine the applicability of image processing system for rapid detection of white blood cell. To get to this result, at first blood sample was taken from 15 broiler chicks, then blood smears were stained with giemsa color and finally were photographed by Canon E500. A total number of 80 RGB color image were created in jpg format, 42 images by confirmation hematology expert was selected and processed by MATLAB 2017a and CellProfiler r10997 softwares. The appearance of WBC such as color, shape and size were considered by MATLAB software. Images were examined in RGB, HSV and L*a*b color spaces. Visual examination and index recording showed that color feature in all color spaces is not useful for detecting WBC. Since the cell nucleus was more distinct in a-layer images than other layers, the image of this layer was used for further processing. The a-layer images converted to binary images by a threshold. Then obtained binary image examined by the implementation of different conditional ring based on their size and shape. Two methods were used based on size; the first ring condition remained four larger objects, and the second method removed areas less than 5000 pixels in the image. In the shape method the interval 1-1.25 was considered to be form factor for the remainder of round objects and in the combined method, the removal of area less than 5000 pixels with an interval 1-1.25 have been considered. Second method had the highest number of diagnostic cells (78 cells of 93). In the image processing by cellprofiler software, by using various modules and typical diameter 18-52 pixel for objects, 78 cells of 93 cell were identified. Generally, the results showed that more advanced machine learning algorithms are required to be test for counting and differential white blood cell diagnostics. Key Words: Color image processing, Morphological image processing, broiler, White blood cells
