Automated microscopic image analysis for leukocytes identification: a survey.

Area of Science:

• Medical Image Analysis
• Hematology
• Computational Pathology

Background:

• Accurate quantification and classification of leukocytes in microscopic images are vital for disease diagnosis, monitoring progression, and drug development.
• Extracting reliable leukocyte data from blood or tissue images presents significant computational challenges.
• Existing methods face issues like poor image background, overlapping cell nuclei, and dimensionality reduction of nuclei.

Purpose of the Study:

• To review and evaluate recent automated methods for leukocyte identification in microscopic images.
• To identify and discuss the constraints and limitations of current leukocyte quantification techniques.
• To present a future research perspective and outline challenges faced by pathologists.

Main Methods:

• Categorization and critical evaluation of recently developed automated leukocyte identification methods.
• Analysis of image normalization, nuclei segmentation, and cell classification techniques.
• Discussion of common challenges including background noise and nuclear overlap.

Main Results:

• A comprehensive overview of current automated leukocyte identification techniques and their performance.
• Identification of key limitations in existing methods, hindering accurate quantification and classification.
• Insights into the practical challenges encountered by pathologists in manual or automated analysis.

Conclusions:

• Automated leukocyte analysis is critical but faces persistent technical hurdles.
• Further research is needed to overcome limitations in segmentation, classification, and handling image artifacts.
• Addressing these challenges will improve diagnostic accuracy and accelerate drug discovery processes.