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Finsler active contours.

John Melonakos¹, Eric Pichon, Sigurd Angenent

¹Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. jmelonak@ece.gatech.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 16, 2008

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This study introduces an enhanced image segmentation method using conformal active contours with directional information. The technique accurately extracts features like roads and blood vessels by defining a well-posed minimization problem.

Area of Science:

Computer Vision
Image Processing
Computational Geometry

Background:

Active contour models are widely used for image segmentation.
Existing conformal active contour frameworks lack directional sensitivity.
Integrating directional information is crucial for segmenting complex structures.

Purpose of the Study:

To augment the conformal active contour framework with directional information for improved image segmentation.
To establish a well-defined minimization problem for directional conformal active contours using Finsler metrics.
To demonstrate the technique's efficacy across diverse imaging modalities.

Main Methods:

Augmenting conformal active contours with position-dependent conformal factors.
Formulating a minimization problem using Finsler metrics to incorporate directionality.

Related Experiment Videos

Employing calculus of variations and dynamic programming for optimal curve extraction.

Main Results:

A novel image segmentation technique is proposed, enhancing the conformal active contour framework.
The method successfully incorporates directional information, leading to a well-defined optimization problem.
Demonstrated successful segmentation of roads, blood vessels, and neural tracts.

Conclusions:

The proposed directional conformal active contour method offers a robust approach to image segmentation.
This technique provides a significant advancement for extracting curvilinear structures in various imaging applications.
The framework is versatile and applicable to aerial, medical, and MRI imagery.