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Quantifying Intermembrane Distances with Serial Image Dilations

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Published on: September 28, 2018

Local region based medical image segmentation using j-divergence measures.

Wanlin Zhu¹, Tianzi Jiang, Xiaobo Li

¹Medical Imaging and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 7, 2007

Summary

This summary is machine-generated.

This study introduces a new variational method using J-divergence for medical image segmentation. The approach enhances robustness against noise by incorporating Gaussian distribution features, showing promising results.

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Area of Science:

Medical Imaging
Computer Vision
Image Processing

Background:

Image segmentation is crucial for medical image analysis.
Existing methods may lack robustness to noise.
Variational formulations offer a powerful framework for segmentation.

Purpose of the Study:

To propose a novel variational formulation for robust medical image segmentation.
To introduce J-divergence as a dissimilarity measure for region comparison.
To enhance algorithm robustness using Gaussian distribution features.

Main Methods:

A novel variational formulation is proposed.
J-divergence (symmetrized Kullback-Leibler divergence) is used for dissimilarity measurement.
Local region intensity is modeled using Gaussian distribution (mean and variance) for noise robustness.

Main Results:

The proposed method was validated on both synthetic and real medical images.
Experimental results demonstrated encouraging performance.
The incorporation of Gaussian features improved robustness to noise.

Conclusions:

The novel variational formulation with J-divergence is effective for medical image segmentation.
The method shows significant promise for improving segmentation accuracy and robustness.
Further applications in medical image analysis are warranted.