Advanced automated classification and segmentation of leukemic cells using simulated optical scanning holography and...

Area of Science:

• Hematology
• Biomedical Optics
• Medical Imaging

Background:

• Leukemia diagnosis faces challenges due to cell heterogeneity and variability in analysis.
• Accurate and rapid cell classification is crucial for effective leukemia management and reducing errors.

Purpose of the Study:

• To develop an automated system for classifying and segmenting leukemic cells.
• To enhance diagnostic accuracy and efficiency in leukemia detection.

Main Methods:

• Utilizing optical scanning holography (OSH) for noninvasive leukocyte phase current capture.
• Employing active contour (AC) models for improved cell segmentation.
• Analyzing maximum amplitude values of phase current for automated classification.

Main Results:

• The integrated OSH and AC approach demonstrated robust segmentation and efficient leukemic cell classification.
• Significant improvements in reliability, speed, and reproducibility were observed compared to existing methods.

Conclusions:

• This novel method offers a reliable, rapid, and systematic solution for accurate leukemia diagnosis.
• The approach facilitates optimized therapeutic management through precise diagnostics.