Global and Local Entropy Based Segmentation Model for Detecting Leukemia in Blood Images

Abstract

The advancement of digital image processing classification is considered an emerging field of disease diagnosis. Various literature concentrated on classification of Leukemia in single white blood cells of smear images. This paper aimed to propose an effective machine learning mechanism for segmentation of leukemia in blood smear. The segmentation is based on estimation of local and global entropy. The proposed technique is stated as Global Local Entropy Histogram Equalization (GLEHE) for classification of leukemia in blood. The GLEHE segments leukemia in blood smear with image histogram. Both global and local features histogram is estimated based on mutual information (MI) for segmentation of blood clot in WBC’s. Further, the proposed GLEHE incorporates geometric feature examination for accurate segmentation of leukemia in blood. For experimental analysis, data were collected from AA-IDB2 database. The parameters considered for analysis are TPR, TNR and accuracy. Existing technique NN, KNN, Random Forest, ACNN, SVM, Chrono-SCA_ACNN and DCNN provides 59.01%, 62.04%, 63.2%, 68.12%, 74.9%, 80% and 80.25% of accuracy. However, the proposed GLEHE provides improved accuracy of 84.64%. Simulation results demonstrated that proposed GLEHE exhibits higher accuracy, TNR and TPR rate than existing technique.