Unsupervised Joint Image Denoising and Active Contour Segmentation in Multidimensional Feature Space

Qi Ge,Hai-Bo Li,Fei Wu,Jingjie Yan,Xiao-Yuan Jing

doi:10.1155/2016/3909645

Abstract

We describe a new method for simultaneous image denoising and level set-based active contour segmentation using multidimensional features. We consider an image to be a surface embedded in a Riemannian manifold. By defining a metric in the embedded space, which in our case includes multidimensional image features as well as a level set-based active contour model, a minimization problem in the image space can be obtained through the Polyakov action framework. The resulting minimization problem is solved with a dual algorithm for efficiency. Benefits of this new method include the fact that it is independent of any artificial “running” parameters, and experiments using both synthetic and real images show that the method is robust with respect to noise and blurry object boundaries.

Highlights

Unsupervised image segmentation is an important problem with many applications in science, including medical imaging
The statistical methods, such as expectation-maximization (EM) algorithm [1] and fuzzy C-means clustering (FCM) algorithm [2], are applied in classifying the pixels based on some particular image features segmentation criteria
All the experiments are run with Matlab code on the PC of CPU 3.2 GHz, RAM 728 M. we show the experiments results for medical image segmentation of Chan-Vese model (CV)

Summary

Introduction

Unsupervised image segmentation is an important problem with many applications in science, including medical imaging. Many works utilize the difference between invariable pixel intensities, as well as their spatial connectivity, in assessing whether two pixels belong to the same object These active contour models based on the level set method [3] classify the pixels by only one image feature, that is, the image intensity based on uniform distribution [4,5,6]. The Polyakov action was introduced in image processing by Sochen et al in [9] This segmentation model is different from the other segmentation methods in two ways. Bresson et al [10] propose active contour models based on the Polyakov action These models map several kinds of features, for example, color and texture, into higher dimensional.

Geometrical Framework Based on Weighted Polyakov Action

The Active Contour Model in Multifeature Space

Active Contour Evolution under the Improved Geometrical

Experimental Results

Conclusion