Magnetostatic Active Contour Model with Classification Method of Sparse Representation

Guoqi Liu,Yifei Dong,Ming Deng,Yihang Liu

doi:10.1155/2020/5438763

Abstract

The active contour model is widely used to segment images. For the classical magnetostatic active contour (MAC) model, the magnetic field is computed based on the detected points by using an edge detector. However, noise and nontarget points are always detected. Thus, MAC is nonrobust to noise and the extracted objects may be deviant from the real objects. In this paper, a magnetostatic active contour model with a classification method of sparse representation is proposed. First, rough edge information is obtained with some edge detectors. Second, the extracted edge contours are divided into two parts by sparse classification, that is, the target object part and the redundant part. Based on the classified target points, a new magnetic field is generated, and contours evolve with MAC to extract the target objects. Experimental results show that the proposed model could decrease the influence of noise and robust segmentation results could be obtained.

Highlights

Image segmentation aims to separate the “target” area and the “background” area of the input image and to extract the interesting part for in-depth understanding and analysis [1, 2]
In [3], a new methodology combines deep learning with the level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. is combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications
active contour model (ACM) can be usually divided into two types: the ACM based on region information [4,5,6,7,8,9,10, 11] and the ACM based on edge information [12,13,14,15,16]. e region-based ACM has been cited and studied by many scholars, because it does not rely on the edge information of the image and can better deal with the weak edge, even the image without edge

Summary

Introduction

Image segmentation aims to separate the “target” area and the “background” area of the input image and to extract the interesting part for in-depth understanding and analysis [1, 2]. E edge-based ACM mainly uses the gradient information of the image and pushes the contour to the target boundary under the action of the defined forces of the curve. In order to speed the contour evolution, a distance regularized level set evolution method (DRLSE) [24] by integrating a regional speeding term is proposed by Li. On the other hand, some models by defining a new external force field on the basis of the GAC model are proposed to improve the robustness of initialization. E magnetostatic active contour (MAC) model [33] is proposed by Xie, and it utilizes the edge information to realize the segmentation of objects. Different from the ACMs based on traditional vector fields, the problem of equilibrium points does not exist in MAC, and it can effectively extract objects with a concave region. In order to solve these two problems, an improved MAC model with a classification method of sparse representation is proposed

Background

Proposed Method

Quantitative Assessment

Findings

Conclusion