Image Segmentation of Acute Myeloid Leukemia Using Multi Otsu Thresholding

Abstract

Acute Myeloid Leukemia (AML) can be identified by utilizing image processing. The stages used in the image processing process include preprocessing, segmentation, and feature extraction. The purpose of this segmentation is to separate objects in the image. One of the image segmentation methods is Multi Otsu Thresholding. This method has a way of working by looking for the threshold value dynamically. Previously there has been researched using the Static Otsu Thresholding method, but the results are still unsatisfactory because the threshold value obtained in the Static Thresholding method is that the threshold value obtained is static, meaning that the threshold value cannot adjust to the brightness level of each image that will be identified. With the background of the segmentation result dissatisfaction factor from the static threshold value, this study aims to carry out the segmentation process to obtain a dynamic threshold value, namely the Multi Otsu Thresholding Method. The Multi Otsu Thresholding method will be applied to segment AML images of M0 and M1 types, with the hope that the segmentation results obtained can be used for the feature extraction process better and are useful for the identification and classification process. Image processing methods used in this research are YCbCr color space, median filter, multi otsu thresholding, and morphological operations. The cell identification process utilizes the cell type classification process using the Naïve Bayes Classifier. The extracted characteristics were WBC (White Blood Cell) diameter, nucleus ratio, and nucleus roundness. Image data to test the process are 29 images of AML M0 and 30 images of AML M1. It was found that the segmentation results using the Multi Otsu Thresholding method can be used for the feature extraction process in AML M0 and AML M1 images and used for the identification process using the Naïve Bayes Classifier resulting in an accuracy of 83.81%, in this case, it has been compared with Static Otsu Thresholding which results with an accuracy of 75.35 %.