In circle detection and measuring problem, there are many general algorithms such as Hough transform, RANSAC, and etc. However, these methods have difficulties in detecting the center and the radius of tubes or other polygonal shapes efficiently because of algorithm complexity, time consumption in calculation. In this paper, we proposed a novel radius measuring algorithm which has a capability to effectively measure the radius of small circles under a lightening noise as well as jig/fixture positioning error. Moreover, our algorithm can be applied to calculate the center point of any polygonal shapes that is very difficult to be implemented by regular circle detection algorithm. In the algorithm we adopt an iterative fuzzy searching method that searches the center of any shapes by fuzzy parameters of distance and orientation from initial search point, which can reduce computational complexity significantly and accurately detect the center point under severe noisy environment. The proposed algorithm has been implemented and tested on both synthetic and real-world part images, and the performance is compared to popular circle detection algorithms to prove the accuracy and effectiveness of the algorithm. From the comparison it is concluded that the proposed algorithm gives an excellent performance in measuring accuracy and computation time.