Maximum Inter Class Variance Segmentation Algorithm Based on Decision Tree

Maximum Inter Class Variance Segmentation Algorithm Based on Decision Tree

Sanli Yi (School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China), Guifang Zhang (School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China) and Jianfeng He (School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China)
Copyright: © 2019 |Pages: 16
DOI: 10.4018/IJISSS.2019040105

Abstract

In image segmentation, there are always some false targets which remain in the segmented image. As the grayscale values of these false targets are quite similar to the grayscale values of the targets of interest, it is very difficult to split them out. And because these false targets exist in the original image, which are not caused by noise or traditional filtering methods, such as median filtering, they cannot be eliminated effectively. It is important to analyze the characteristics of false targets, so the false targets can be removed. In addition, it should be noted that the targets of interest cannot be affected when the false targets are removed. In order to overcome above problems, a maximum inter-class variance segmentation algorithm based on a decision tree is proposed. In this method, the decision tree classification algorithm and the maximum inter-class variance segmentation algorithm are combined. First, the maximum inter-class variance algorithm is used to segment the image, and then a decision tree is constructed according to the attributes of regions in the segmented image. Finally, according to the decision tree, the regions of the segmented image are divided into three categories, including large target regions, small target regions and false target regions, so that the false target regions are removed. The proposed algorithm can eliminate the false targets and improve the segmentation accuracy effectively. In order to demonstrate the effectiveness of the algorithm proposed in this article, the proposed method is compared with some frequently used false target removal approaches. Experimental results show that the proposed algorithm can achieve better results than other algorithms.
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Introduction

Image segmentation is an important and complicated technique with many applications in image processing and analysis, such as computer vision, pattern recognition, medical image processing. The aim of image segmentation is to extract regions of interest from complex scenes.

During the past decades, many different kinds of image segmentation approaches have been proposed. Generally, image segmentation methods can be divided into four types: image thresholding, image boundary based, image region based and mixing segmentation technology. Among these segmentation approaches, the thresholding method is widely used due to its simplicity and ease of implementation (Goh, Basah, Yazid, Safar, & Saad, 2018; Mittal & Saraswat, 2018). Its main idea is choosing a threshold which can distinguish the image background and target in the image. And the maximum inter-class variance segmentation algorithm (Otsu, 1978) is one of the classical image thresholding segmentation algorithms. It is a kind of global automatic nonparametric unsupervised algorithm and widely used, which takes the maximum inter class variance as measure criterion. While there is still a false targets problem in the maximum inter class variance segmentation algorithm. And the study of this paper is based on the segmented image which are segmented by the maximum inter class variance segmentation algorithm. After segmented by the maximum inter class variance segmentation algorithm, the segmented image is obtained which is composed of many independent regions (Li & Feng, 2016). Each region in the segmented image corresponds to a target. These targets contain not only the targets of interest, but also the false targets. And these false targets are not caused by noise but exist in the original image. It is difficult to split these false targets out effectively because their grayscale values are similar to the grayscale values of the regions of interest. For the segmented image, these false targets are interference, and the removal of false targets plays a very important role for image segmentation.

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