Advanced automated classification and segmentation of leukemic cells using simulated optical scanning holography and active contour methods

Abstract

Significance: Leukemia, a complex hematological cancer, poses significant diagnostic challenges due to the heterogeneity of leukemic cells, inter-observer variability, and lack of standardized analysis methodology. Accurate and rapid cell classification is essential to improve clinical management, optimize treatment, and reduce diagnostic errors. Aim: We propose an innovative approach combining optical scanning holography (OSH) and active contour (AC) models to automate the classification and segmentation of leukemic cells with increased accuracy. Approach: OSH is used to capture the phase current of leukocytes, providing a cost-effective, noninvasive, and simplified alternative to conventional techniques. AC models are used to improve cell segmentation. Analysis of the maximum amplitude values of the phase current allows rapid and fully automated classification. Results: The proposed approach shows a significant improvement in terms of reliability, speed, and reproducibility compared with existing methods. The integration of OSH and AC enables robust segmentation and efficient classification of leukemic cells. Conclusion: This method provides a reliable, rapid, and systematic solution for the accurate diagnosis of leukemia, enabling optimized therapeutic management.