Application of Watershed Algorithm and Gray Level Co-Occurrence Matrix in Leukemia Cells Images

A long with the development of technology, the image of a sample of leukemia can be digitally processed to reduce the level of human error in diagnosing the disease. This research conducted by designing the image processing system on two types of leukemia, there are Acute Myelogenous Leukemia (AML) and Normal cell images by applying Watershed segmentation methods and feature extraction Gray Level Co-Occurrence Matrix (GLCM). The system was designed to find out how effective these methods to be continue into the classification process. The results of testing the application of these methods are the accuracy of the watershed segmentation method for this kind of Normal class was 90.4% with the average computing time of 0.89 seconds, and for the class of AML is 100% with the average computation time of 0.94 seconds. Application of the method GLCM has a significant difference the two types of leukemia were examine for each value extraction features with faster computing time, average 0.0060 seconds of computing time for this kind of Normal images. Whereas, for the AML class with average computation time was 0.0054 seconds.