Weak edge detection of CT image based on improved algorithm of fuzzy theory

In order to solve the weak edge detection of traditional industrial CT images shortcomings of poor detection effect and low speed, a detection method of step fuzzy inference algorithm and another method of improved defuzzification algorithm were researched. Compared with overall reasoning method, the step fuzzy inference algorithm selected similarity, gradient and consistency as blur characteristics. And the reasoning process used Mandani reasoning to conduct step fuzzy reasoning, which was based on simplified inference rule tables. Improved defuzzification algorithm was proposed in the solution process. According to membership function figure, the two methods were verified by experiments. The results showed that the step fuzzy inference algorithm was better in the weak edge detection, while the improved defuzzification algorithm greatly increased the computing speed on the premise of accuracy.