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Related Experiment Video

Updated: May 18, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Modified gradient search for level set based image segmentation.

Thord Andersson¹, Gunnar Läthén, Reiner Lenz

¹Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden. thord.andersson@liu.se

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 28, 2012

Summary

This summary is machine-generated.

This study introduces modified gradient descent methods for image segmentation using level set methods. These new approaches improve convergence speed and reduce sensitivity to local minima, enhancing segmentation accuracy.

Related Experiment Videos

Last Updated: May 18, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Computer Vision
Image Processing
Computational Mathematics

Background:

Level set methods are widely used for image segmentation.
Gradient descent is a common optimization technique for minimizing cost functionals in segmentation.
Standard gradient descent methods suffer from slow convergence and sensitivity to local minima.

Purpose of the Study:

To improve the performance of gradient descent in level set image segmentation.
To address the limitations of slow convergence and local minima sensitivity.
To introduce machine learning-inspired optimization techniques into level set methods.

Main Methods:

Proposed two modified gradient descent methods: one with a momentum term and another based on resilient propagation.
Applied these methods to level set-based image segmentation.
Conducted experiments on 2D/3D real and synthetic data with ground truth.

Main Results:

The modified gradient descent methods demonstrated reduced sensitivity to local optima.
Increased convergence rates were observed compared to standard gradient descent.
Parameter sensitivity of the proposed methods was investigated.

Conclusions:

Modified gradient descent methods enhance level set image segmentation by improving convergence and robustness.
These simple modifications are compatible with existing level set implementations.
The proposed methods offer a practical improvement for image segmentation tasks.