Maximum Inter Class Variance Segmentation Algorithm Based on Decision Tree

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.