Segmentation of biomedical images based on a computational topology framework

Area of Science:

• Computational Topology
• Image Analysis
• Digital Image Processing

Background:

• Homology groups offer insights into topological space connectivity and features like holes.
• These topological characteristics have applications in image structure analysis and classification.
• Computing homological features involves analyzing relationships between points in a topological space.

Purpose of the Study:

• To present a technique for denoising persistence diagrams.
• To reconstruct segmented object shapes using information from persistence diagrams.
• To demonstrate the application in segmenting cell nuclei in histological images.

Main Methods:

• Constructing a topological space from image data for feasible homology group computation.
• Applying topological denoising by aggregating trivial classes on the persistence diagram.
• Utilizing a growing seed algorithm informed by persistence diagram data for shape reconstruction.

Main Results:

• Achieved topological denoising of persistence diagrams.
• Successfully reconstructed segmented cell structures using a growing seed algorithm.
• Demonstrated efficacy in a case study of cell nuclei segmentation.

Conclusions:

• The proposed approach enables effective topological denoising and shape reconstruction for image segmentation.
• This method leverages topological data analysis for enhanced segmentation of biological structures.
• The technique shows promise for practical applications in image analysis and digital pathology.