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A partition-based active contour model incorporating local information for image segmentation.

Jiao Shi¹, Jiaji Wu¹, Anand Paul²

¹Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi 710071, China.

Thescientificworldjournal

|August 23, 2014

Summary

This study introduces an enhanced active contour model for image segmentation, improving accuracy on intensity inhomogeneous images by considering local image statistics and adaptively updating contour boundaries.

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Area of Science:

Computer Vision
Image Processing
Computational Imaging

Background:

Traditional active contour models assume piecewise-constant image intensities, limiting their effectiveness on real-world images with intensity inhomogeneity.
Intensity inhomogeneity presents significant challenges for accurate image segmentation using conventional methods.

Purpose of the Study:

To propose an enhanced active contour model capable of segmenting images with intensity inhomogeneity.
To adapt the contour evolution process by considering local image statistics and adaptively identifying regions near contour boundaries.

Main Methods:

The proposed model utilizes local image information and a milder assumption of statistical homogeneity within local regions.
It adaptively detects regions near contour boundaries for iterative updates, aligning with curve evolution theory.
Pixels within these selected regions are updated in each iteration, allowing gradual contour evolution.

Main Results:

Experimental results on synthetic and real-world images demonstrate the model's effectiveness in handling intensity inhomogeneity.
The enhanced active contour model shows significant advantages over traditional methods in challenging image segmentation scenarios.

Conclusions:

The developed active contour model successfully addresses the limitations of existing methods when segmenting intensity inhomogeneous images.
The approach offers a more robust and accurate solution for image segmentation in practical applications.