Optimized Segmentation of Microscopic Images by Otsu’ Method to Find White Blood Cells

Abstract

In the medical field, microscopic image-based investigations are broadly used to examine cell morphology and for disease diagnosis. In this article white blood cells (WBC) are identified from microscopic images by image segmentation. One of the important segmentation methods is multilevel thresholding of an image, in this process pixels are grouped into different classes depends on thresholding levels. The selection of threshold levels affects the efficiency of segmentation. Otsu’s method is a significant and important segmentation technique based on the multi-threshold of the histogram. Optimization techniques can be used to compute optimized threshold levels, Harmony Search Algorithm (HSA) is used for this purpose. From the color, microscopic image red, green, and blue components are extracted and segmentation to find white blood cells, and from the same images gray, hue, saturation, and intensity components also extracted pathological features. After segmentation Morphological opening and closing are applied for an efficient finding of white cells, from results the green component of microscopic images is giving efficient results. Results are described by using standard deviation (STDR), mean square error (MSE), Mean of fitness of algorithm (MOFIT), time for execution, and FITNESS function.