Circle fitting methods are extensively utilized in various industries, particularly in quality control processes and design applications. The effectiveness of these algorithms can be significantly compromised when the point sets to be predicted are noisy. To mitigate this issue, outlier detection and removal algorithms are often applied before the circle fitting procedure. This study introduces the Polar Coordinate-Based Outlier Detection (PCOD) algorithm, which can be effectively employed in circle fitting applications. In the proposed approach, the point set is first transformed into polar coordinates, followed by the calculation of both local and global standard deviations. Outliers are then identified by comparing local mean values with the global standard deviation. The practicality and efficiency of the proposed method are demonstrated by focusing on the high-precision diameter measurement of industrial washer parts. Images from a machine vision system are processed through preprocessing steps, including sub-pixel edge detection. The resulting sub-pixel edge points are then cleaned using the proposed outlier detection and removal algorithm, after which circle fitting is performed. A comparison is made using ten different circle fitting algorithms and five distinct outlier detection methods. The results indicate that the proposed method outperforms the other approaches, delivering the best performance in terms of accuracy within the dataset, thereby demonstrating its potential for enhancing circle fitting applications in industrial environments.