An efficient peripheral blood smear image analysis technique for Leukemia detection

Patients with leukaemia present with decreased haemoglobin and elevated WBC count in around 60-70% of cases. Peripheral blood smear (PBS) examination reveals around 40-95% blast cells in usually most of the Acute Leukemia patients. The digital image processing techniques have successfully contributed to generate new methods for cell analysis has lead into more accurate and reliable systems for disease diagnosis. But high variability on cell shape, size, edge, and localization complicates the data extraction process. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability.