Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

Duraiswamy Umamaheswari

doi:10.17485/ijst/v13i45.328

Abstract

Background/Objectives: To present an accurate quantitative approach based two-phase algorithm to count both the leukocytes and erythrocytes for identifying the severity of leukaemia in the human body. Methods/Statistical analysis: The algorithm is having two-phases with the first phase meant for recognizing and counting the leukocytes using the thresholding based segmentation technique that focuses on the intensity values of pixels of the greyscale blood smear images; whereas the second phase recognizes the erythrocytes by their circular shape using Circular Hough Transform (CHT) method. The system experiments with 26 stained blood smear images from the ALL-IDB1 benchmark dataset. Findings: The first phase of the algorithm achieves 99.41 per cent overall accuracy in leukocytes detection and in the second phase 99.76 per cent overall accuracy is attained in erythrocytes detection. Novelty/Applications: This proposal applies Circular Hough Transform in detecting the erythrocytes by adjusting the radius of the circle according to the magnification rate of the sample image. Keywords: Circular Hough transform; cell count; image processing; Leukaemia; Leukocytes; Erythrocytes

Highlights

Many research proposals evident that leukocytes’ counting helps in detecting leukaemia
In[1] the preprocessing is done by standardizing the color space, and the segmentation is performed by applying color component subtraction
The dataset chosen to do this experiment is ALL-IDB1 constructed by Donida Labati. This is an open-source benchmark dataset issued on request that contains 108 peripheral blood smear images in JPG format

Summary

Introduction

Many research proposals evident that leukocytes’ counting helps in detecting leukaemia. It is essential to find out the erythrocytes counting for providing the largest possible accuracy. In[1] the preprocessing is done by standardizing the color space, and the segmentation is performed by applying color component subtraction. Color component subtraction is experimented on RGB, CMYK, and HSV color spaces. The subtraction of G component of RGB color space and S component of CMYK color space has given 97.79 per cent of segmentation accuracy. The algorithm has used shape and color based features to do the segmentation of leukocytes in the accuracy rate of 99.86 per cent and erythrocytes at the rate of 93.4 per cent. That method has not achieved the highest accuracy in detecting erythrocytes, because the applied watershed algorithm less performs at detecting the ridgelines of the overlapping and crowded erythrocytes. It is apparent that the shape, size, and texture of these two blood components are differing to a great extent[3,4]

Methods

Results

Conclusion