Identification and red blood cell classification using computer aided system to diagnose blood disorders

摘要

Red blood cell count plays a vital role in identifying the overall health of the patient. Mature Red blood cells undergo morphological changes when blood disorder exists. Automated and Manual techniques exist in the market to count the number of RBCs(Red blood cells). Manual counting involves the use of Hemocytometer to count the blood cells. The conventional method of placing the smear under a microscope and counting the cells manually leads to erroneous results and medical laboratory technicians are put under stress. Automated counters fail to identify abnormal cells. A computer aided system will help to attain precise results in less amount of time. This research work proposes an image processing technique to separate the Red blood cell from other components of blood. It aims to examine and process the blood smear image, in order to support the classification of Red blood cells into 11 categories. K-Medoids algorithm which is robust to external noise is used to extract the WBCs from the image. The granulometric analysis is used to separate the Red blood cells from White blood cells. Feature extraction is done to obtain the significant features that help in classification. The classification results help in diagnosing the diseases like Sickle Cell Anemia, Hereditary Spherocytosis, Normochromic Anemia, Iron Deficiency Anemia, Megaloblastic Anemia and Hypochromic Anemia within few seconds.