Maximum Likelihood Estimation for Disk Image Parameters

We present a novel technique for estimating disk parameters (the center and the radius) from its 2D image. It is based on the maximal likelihood approach utilizing both edge pixels coordinates and the image intensity gradients. We emphasize the following advantages of our likelihood model. It has closed-form formulae for estimating the parameters, therefore, requiring less computational resources than iterative algorithms. The likelihood model naturally distinguishes outer and inner annulus edges. The proposed technique was evaluated on both synthetic and real data.