Shape Prior Embedded Level Set Model for Image Segmentation

Wansuo Liu,Dengwei Wang,Wenjun Shi

doi:10.1155/2019/9014217

Abstract

This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail.

Highlights

Image segmentation is an important intermediate step in the field of computer vision, which aims to partition a given image into a set of nonoverlapping regions where the internal pixels are homogeneous with respect to intensity, color, texture, motion, semantics, etc
Among various types of image segmentation theories, the level set model (LSM) are widely used because they are capable of outputting closed and smooth target contours and can naturally handle topology changes. e core idea of this type of method is constructed by Osher and Sethian [1], and its key is to implicitly represent contour as the zero level set of a higher dimensional level set function (LSF) and compute a time-dependent equation to obtain a deforming surface
When optimizing the edge stop function, we introduce the thought of probability weighting. e existence of the statistical probability term makes the edge stop function contain region information. erefore, our model has a stronger weak edge capture capability; when optimizing the coefficient of the weighted area term, we abandon the original constant coefficient because the constant-type coefficient makes the model only evolve in a single direction, which is obviously problematic in practical applications, and the most obvious phenomenon is that the segmentation result is highly related with the initialization

Summary

Introduction

Image segmentation is an important intermediate step in the field of computer vision, which aims to partition a given image into a set of nonoverlapping regions where the internal pixels are homogeneous with respect to intensity, color, texture, motion, semantics, etc. When there is a certain amount of noise in the image or the edge of the target is very blurred (the corresponding gradient amplitude is very small), the evolution process of such models generally exhibits the following problems: falling into local minima (easy to be pulled by background noise information to the wrong location), edge leakage (unable to locate the blurred target edges), and sensitive to initialization (the final segmentation result is directly related to the position and shape of the initial contour)

Results

Discussion

Conclusion