Active contour model coupling with higher order diffusion for medical image segmentation.

Guodong Wang,Jie Xu,Qian Dong,Zhenkuan Pan

doi:10.1155/2014/237648

Guodong Wang, Jie Xu + Show 2 more

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https://doi.org/10.1155/2014/237648

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Abstract

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.

Highlights

Medical images are popular in real world because they give intuitive expression
The active contour model has been increasingly applied to image segmentation in the past decade, because it provides very good frameworks of variational image segmentations
Chan-Vese (CV) model [1] is the most popular active contour model for image segmentation based on the feature of mean gray value of different regions

Summary

Introduction

Medical images are popular in real world because they give intuitive expression. With the development of image processing, computer-aided diagnosis are more important with the ever increasing images. Chan-Vese (CV) model [1] is the most popular active contour model for image segmentation based on the feature of mean gray value of different regions. The method is proposed for medical images with complex topological structure, strong contrast, and low noise characteristics It makes full use of International Journal of Biomedical Imaging the image area information, builds an energy model, and uses variation gradient information to establish a global energy model to get the minimization value. Yao and Cheng [11] use adjustable method for medical image segmentation They combine active contour model with diffusion filter for multiobject segmentation of the noisy image. Incorporate edge information alone is not enough, we incorporate higher order diffusion term into the active contour model aiding segmentation. We propose a new active contour model with gradient and higher-order information of the image.

Higher Diffusion and Active Contour Model

Active Contour Model Coupling with Higher-Order Diffusion

Numerical Experiments

Conclusion