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A generic approach for cell segmentation based on Gabor filtering and area-constrained ultimate erosion
1College of Electrical and Electronic Engineering, Shandong University of Technology, China.
Artificial Intelligence in Medicine
|August 24, 2020
Summary
This study introduces a novel cell segmentation method using image gradients and Gabor filters for accurate cell counting. The approach robustly segments diverse cell types, meeting stringent accuracy demands in microscopy applications.
Area of Science:
- Microscopy Image Analysis
- Computational Biology
- Biomedical Imaging
Background:
- Increasing demand for accurate cell segmentation in microscopy.
- Limitations of traditional methods in handling cell diversity and accuracy requirements.
- Need for a robust, generic cell segmentation approach.
Purpose of the Study:
- To propose a generic and robust approach for segmenting various cell types.
- To accurately count the total number of cells in microscopic images.
- To overcome limitations of intensity-based segmentation methods.
Main Methods:
- Utilizing cell gradients instead of intensity for segmentation.
- Applying Gabor filters to enhance gradient image uniformity.
- Employing slope difference distribution for optimal threshold selection.
- Using area-constrained ultimate erosion for separating connected cells.
Main Results:
- The proposed method demonstrates robust segmentation across twelve diverse cell types.
- Experimental results indicate high accuracy in cell counting.
- The approach effectively addresses global intensity variations.
Conclusions:
- The developed method offers a promising solution for accurate cell segmentation and counting.
- It meets the strict accuracy requirements for various microscopy applications.
- The gradient-based approach provides enhanced robustness compared to traditional methods.
