A comparison of platelets classification from digitalization microscopic peripheral blood smear

Thrombocyte disease is usually caused by abnormalities, such as abnormalities based on the number and morphological deformities of platelets. Examples of platelet abnormalities include small platelets in Wiskottldrich syndrome, giant platelets in some chronic myeloproliferative diseases, Benard Soulier syndrome and Macrothrombocytopenia in gray platelet syndrome. The usual problem of automatic FBC analysis is that undetectable morphological abnormalities of platelets so the microscopic examination is required using peripheral blood smear. But microscopic examination also has some weakness such as subjective depend on medical analyst/pathologist. We propose an accurate method to classify plateles from digitalization microscopic peripheral blood smear using combination of second order statistic feature extraction and comparing several methods. The comparing methods are K-Nearest Neighbor (KNN) and Learning Vector Quantization (LVQ). In this feature extraction, we use Gray Level Co-occurrence Matrix (GLCM) to get Angular Second Moment (ASM), Invers Different Moment (IDM) and entropi values. Those values will be inserted as input in KNN classifier method to classify blood cell in peripheral blood smear. Classify of cells based on feature extraction values is divided into three classes (leukocytes, normal platelets and giant platelets). Based on the result of experiments, both of methods can classify platelets on all color channels with average accuracy are 83.67% for KNN and 74.75% for LVQ. So, The KNN classification method is better able than LVQ to classify platelets in peripheral blood smear.