A landmark-based algorithm for automatic pattern recognition and abnormality detection

TL;DR

This paper introduces a landmark-based algorithm for automatic pattern recognition and abnormality detection in images, utilizing template matching and Bayesian inference to identify structural anomalies with high consistency.

Contribution

It presents a novel landmark-based algorithm that estimates group features and detects abnormalities using residual momentum and Bayesian inference, applicable to brain imaging.

Findings

High consistency with existing literature in brain structure abnormality detection

Effective use of residual momentum for local coordinate system estimation

Demonstrated applicability on a small brain image database

Abstract

We study a class of mathematical and statistical algorithms with the aim of establishing a computer-based framework for fast and reliable automatic abnormality detection on landmark represented image templates. Under this framework, we apply a landmark-based algorithm for finding a group average as an estimator that is said to best represent the common features of the group in study. This algorithm extracts information of momentum at each landmark through the process of template matching. If ever converges, the proposed algorithm produces a local coordinate system for each member of the observing group, in terms of the residual momentum. We use a Bayesian approach on the collected residual momentum representations for making inference. For illustration, we apply this framework to a small database of brain images for detecting structure abnormality. The brain structure changes identified by our framework are highly consistent with studies in the literature.