An Improved Region Growing Method for Medical Image Selection and Evaluation Based on Canny Edge Detection

Region growing method is largely used in medical image segmentation. However, there are still some difficulties that have not been well solved as the medical image is complicated. Especially, some of the segmentation methods do well in one kind of image segmentation, but it is poor when it is applied to another image. In order to improve this condition, the article involves an improved method to select the best segmentation methods. It uses three kinds of region growing methods to segment the origin medical image. Canny edge detection is adopted to evaluate the performance of edges locating and decide which is the most suitable. Experiment result proves that this method is very effective. It not only expands the region growing method's applying, but also makes the segmentation result more precisely.

I. Introduction

With the development of society and the improving of life, people begin to concern their health more than ever before. So people require more precisely diagnose and cancer predicted possibility. When the medical image data is introduced to the cancer diagnose, more precisely diagnose and predicted become truth. And medical image segmentation technology is one of the most important operations in medical image that are developed very rapidly.