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Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

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Published on: November 11, 2020

Edge detection in microscopy images using curvelets.

Tobias Gebäck¹, Petros Koumoutsakos

¹Computational Science, ETH Zürich, Universitätstrasse 6, CAB H69,2, ETH Zürich, CH-8092 Zürich, Switzerland. tobias.gebaeck@inf.ethz.ch

BMC Bioinformatics

|March 5, 2009

Summary

A new curvelet transform method enhances edge detection in microscopy images, improving the identification of elongated structures. This approach offers a competitive and novel solution for analyzing cellular and multicellular images.

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Area of Science:

Microscopy image analysis
Computational imaging
Biomedical engineering

Background:

Efficiently detecting edges and elongated features in microscopy images remains a significant challenge.
Current imaging technologies require time-consuming manual analysis for intracellular and multicellular structures.

Purpose of the Study:

To develop a novel, automated method for improved edge and feature detection in microscopy images.
To enhance the analysis of intracellular and multicellular structures using light and electron microscopy.

Main Methods:

Utilizing the discrete curvelet transform to extract a directional field indicating edge location and orientation.
Processing the directional field with Canny algorithm steps (non-maximal suppression, thresholding) for edge tracing.
Optional extension of detected edges along curvelet directions for improved connectivity.

Main Results:

The curvelet-based method outperforms Canny and Gabor filter edge detectors for larger, elongated structures.
The method successfully identifies edges composed of multiple step or ridge features.
A directional field accurately indicates edge location and direction.

Conclusions:

The proposed curvelet-based edge detection is a novel and competitive approach for imaging challenges.
This methodology and software are expected to facilitate and improve edge detection in microscopy.
The technique offers enhanced capabilities for analyzing complex biological structures.