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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Iterative graph cuts for image segmentation with a nonlinear statistical shape prior.

Joshua C Chang¹, Tom Chou²

¹Mathematical Biosciences Institute, The Ohio State University, Jennings Hall, 3rd Floor, 1735 Neil Avenue, Columbus, Ohio 43210.

Journal of Mathematical Imaging and Vision

|March 29, 2014

Summary

This summary is machine-generated.

Shape-based regularization uses kernel density estimation for image object delineation. This study recasts the energy functional for efficient minimization using graph cuts, improving noisy image analysis.

Keywords:

Image segmentation MM energy minimization graph cuts kernel density estimation statistical shape prior

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Shape-based regularization is effective for object delineation in noisy images using prior shape knowledge.
Kernel density estimation (KDE) is a natural method for specifying shape priors from a collection of shapes.

Purpose of the Study:

To develop a method for minimizing energy functionals derived from KDE shape priors.
To enable efficient and iterative minimization using graph cut algorithms.

Main Methods:

Recasting the energy functional from KDE into a minimizable form.
Utilizing graph cuts for iterative optimization.

Main Results:

The proposed method allows for efficient minimization of KDE-based shape priors.
The approach is suitable for iterative optimization using graph cuts.

Conclusions:

The study presents a novel approach to overcome limitations in minimizing KDE-based shape priors.
This method enhances the applicability of shape-based regularization in image analysis through efficient graph cut optimization.