X-ray image segmentation based on genetic algorithm and maximum fuzzy entropy
Xin Wang, Brian Stephen Wong, Chen Guan Tui · 2005
The X-ray radiographic testing method is often used for detecting defects as a non-destructive testing method (NDT). In many cases, NDT is used for aircraft components, welds, etc. Hence, the backgrounds are always more complex than a piece of steel. It is difficult to detect defects using conventional image processing methods. In this paper, we propose a genetic algorithm to find the optimal thresholds to segment X-ray images. In our algorithms, after obtaining the X-ray image, we firstly use adaptive histogram equalization technique and wavelet thresholding to improve the quality of the radiographic image. Then the image is divided into three parts, namely dark, gray and white part. The fuzzy region of their member functions can be determined by maximizing fuzzy entropy. The procedure to find the optimal combination of all the fuzzy parameters is implemented by genetic algorithm, which can overcome the computational complexity problem. The experimental results show that our proposed method gives good performance for X-ray image.