Identification and Red Blood Cell Automated Counting from Blood Smear Images using Computer Aided System

Abstract

Objectives: Red blood cell count plays a vital role in identifying the overall health of the patient. Hospitals use the Haemocytometer to count the blood cells. Conventional method of placing the smear under microscope and counting the cells manually leads to erroneous results and medical laboratory technicians are put under stress. A computer aided system will help to attain precise results in less amount of time. It proposes an image processing technique for counting the number of Red blood cells. This aims to examine and process the blood smear image, in order to support the counting of red blood cells and identify the number of normal and abnormal cells in the image automatically. Methods/Statistical Analysis: K-Medoids algorithm which is robust to external noise is used to extract the WBCs from the image. Granulometric analysis is used to separate the Red blood cells from White blood cells. The Red blood cells obtained are counted using the Labeling algorithm and Circular Hough Transform. The radius range for the circle drawing algorithm is estimated by computing the distance of the pixels from the boundary which automates the entire algorithm. Findings: A comparison is done between the counts obtained using the Labelling algorithm and Circular Hough Transform. Results of the work showed that Circular Hough Transform was more accurate in counting the Red blood cells than the Labelling algorithm as it was successful in identifying even the overlapping cells. The work also intends to compare the results of cell count done using the proposed methodology and manual approach. The results showed that the proposed cell counting approach was much faster and accurate than the manual approach. The work is designed to address all the drawbacks of the previous research work. Novelty/Improvement: It can be extended to extract various texture and shape features of abnormal cells identified so that diseases like Anemia of inflammation and chronic disease can be detected at the earliest.