# Maximum likelihood estimation for disk image parameters

## TL;DR

This paper introduces a maximum likelihood method for estimating disk parameters from 2D images, leveraging edge pixels and intensity gradients, offering computational efficiency and clear edge distinction.

## Contribution

The paper presents a new likelihood-based approach with closed-form solutions for disk parameter estimation, improving efficiency and edge differentiation over existing methods.

## Key findings

- Effective on synthetic and real data
- Requires less computational resources
- Accurately distinguishes inner and outer edges

## Abstract

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