Identification and Counting White Blood Cells and Red Blood Cells using Image Processing Case Study of Leukemia

TL;DR

This paper presents an image processing approach combined with a fuzzy rule-based system to identify and count white and red blood cells, aiming to diagnose leukemia types efficiently and accurately.

Contribution

It introduces a novel method using morphology-based image processing and fuzzy logic for leukemia diagnosis, reducing manual effort and improving speed.

Findings

Achieved 83.65% accuracy in leukemia classification

Used 104 blood cell images for testing

Applied thresholding, edge detection, and fuzzy rules

Abstract

Leukemia is diagnosed with complete blood counts which is by calculating all blood cells and compare the number of white blood cells (White Blood Cells / WBC) and red blood cells (Red Blood Cells / RBC). Information obtained from a complete blood count, has become a cornerstone in the hematology laboratory for diagnostic purposes and monitoring of hematological disorders. However, the traditional procedure for counting blood cells manually requires effort and a long time, therefore this method is one of the most expensive routine tests in laboratory hematology clinic. Solution for such kind of time consuming task and necessity of data tracability can be found in image processing techniques based on blood cell morphology . This study aims to identify Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia type M3 (AML M3) using Fuzzy Rule Based System based on morphology of white blood cells. Characteristic parameters witch extractedare WBC Area, Nucleus and Granule Ratio of white blood cells. Image processing algorithms such as thresholding, Canny edge detection and color identification filters are used.Then for identification of ALL, AML M3 and Healthy cells used Fuzzy Rule Based System with Sugeno method. In the testing process used 104 images out of which 29 ALL - Positive, 50 AML M3 - Positive and 25 Healthy cells. Test results showed 83.65 % accuracy .