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An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images.

Vipasha Abrol¹, Sabrina Dhalla¹, Savita Gupta¹

¹UIET, Panjab University, Chandigarh, India.

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|June 26, 2023

Summary

This summary is machine-generated.

This study introduces an improved method for segmenting leukocytes in blood smear images to aid leukemia diagnosis. The enhanced algorithm achieves 94% precision, with the HSV color space showing superior performance in image segmentation.

Keywords:

Bounding box Color spaces Image processing Leukocytes Marker-based watershed algorithm Segmentation

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Area of Science:

Medical image analysis
Hematology
Computational pathology

Background:

Leukemia diagnosis relies on identifying abnormal blood cell counts.
Automated analysis of peripheral blood smear (PBS) images is crucial for swift leukemia detection.
Accurate segmentation of leukocytes is a critical first step in automated PBS image analysis.

Purpose of the Study:

To present a robust algorithm for segmenting leukocytes in microscopic peripheral blood smear images.
To evaluate the performance of leukocyte segmentation across different color spaces.
To enhance the accuracy of automated leukemia diagnosis through improved image processing.

Main Methods:

Developed a segmentation algorithm utilizing a marker-based watershed approach and peak local maxima.
Employed three distinct color spaces for image enhancement and analysis.
Tested the algorithm on diverse datasets with varying image characteristics (color, resolution, magnification).

Main Results:

Achieved an average precision of 94% across all evaluated color spaces.
The HSV color space demonstrated superior performance in Structural Similarity Index Metric (SSIM) and recall compared to other spaces.
Color space correction techniques were found to improve the overall accuracy of the proposed methodology.

Conclusions:

The proposed segmentation algorithm effectively identifies leukocytes in PBS images.
The HSV color space is recommended for enhanced leukocyte segmentation accuracy.
This research provides valuable insights for developing more precise automated leukemia diagnostic tools.