An Improved Level Set Method on the Multiscale Edges

Yao Su,Tong Peng,Dan Wang,Kun He

doi:10.3390/sym12101650

Abstract

The level set method can segment symmetrical or asymmetrical objects in real images according to image features. However, the segmentation performance varies with feature scale. In order to improve the segmentation effect, we propose an improved level set method on the multiscale edges, which combines the level set method with image multi-scale decomposition to form a unified model. In this model, the segmentation relies on multiscale edges, and the multiscale edges depend on multiscale decomposition. A novel total variation regularization is proposed in multiscale decomposition to preserve edges. The multiscale edges obtained by the multiscale decomposition are integrated into the segmentation process, and the object can be easily extracted from a proper scale. Experimental results indicate that this method has superior performance in precision, recall and F-measure, compared with relative edge-based segmentation methods, and is insensitive to noise and inhomogeneous sub-regions.

Highlights

Image segmentation is a technique of partitioning an image into disjoint regions [1]
According to features for segmentation, image segmentation methods can be divided into two categories, which are based on the learned features and based on the artificial features
In order to test the segmentation performance using the proposed method on real images with strong or weak edges, experiments were carried out to compare this with relative level set models and other edge-based segmentation models, such as Li’s model [16], SDREL model [19], DCLSM model [25]

Summary

Introduction

Image segmentation is a technique of partitioning an image into disjoint regions [1]. The learned features are generally obtained by the deep learning method from the training, and the artificial features generally are low-level features of an image. The segmentation methods based on learned features can extract the objects by using the features obtained from deep learning [7] on the training set. These segmentation methods usually require a large amounts of training data consisting of objects that may appear in an image. The training data are limited in many specific tasks In this case, they cannot replace the methods based on the artificial features. Compared with the traditional segmentation algorithms, FCNs can achieve out-performance for the objects with massive samples, but not for the objects with small training

Methods

Results

Conclusion