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

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.