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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
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Automated segmentation of cellular images using an effective region force.
Khadeejah Mohiuddin1, Justin W L Wan1
1University of Waterloo, Waterloo, Canada.
Journal of Medical Imaging (Bellingham, Wash.)
|October 23, 2018
Summary
We developed two novel graph-partitioning algorithms for automated cell image segmentation. These methods effectively segment challenging bright-field images and demonstrate robust performance across diverse imaging modalities.
Area of Science:
- Cell biology
- Image analysis
- Computational biology
Background:
- Automated segmentation of microscopic cellular images is crucial for understanding cell behavior.
- Segmenting bright-field cell images is challenging due to low contrast, missing boundaries, and broken halos.
Purpose of the Study:
- To present two novel algorithms for automated segmentation of cellular images.
- To address the difficulties in segmenting challenging bright-field microscopy images.
Main Methods:
- A graph-partitioning approach modeling each pixel as a node in a weighted graph.
- Two models combining region force with Laplacian and total variation boundary forces.
- Region force interpreted as conditional probability using a small set of labeled background pixels.
Main Results:
- Both algorithms achieved good results on bright-field images.
- The Laplacian-based algorithm showed faster performance and was tested on diverse datasets.
- Consistent performance was observed across various cell image features, including shape, size, contrast, and noise levels.
Conclusions:
- The proposed graph-partitioning algorithms provide effective solutions for automated cell image segmentation.
- The Laplacian-based method offers a practical and versatile tool for analyzing various cell imaging data.
- These algorithms advance the automation of cell behavior studies through improved image analysis.
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