A landmark-based algorithm for automatic pattern recognition and abnormality detection
This work addresses the need for fast and reliable abnormality detection in medical imaging, but it appears incremental as it builds on existing landmark-based and Bayesian methods.
The authors tackled the problem of automatic abnormality detection in medical images by developing a landmark-based algorithm that estimates a group average and uses residual momentum for inference, achieving high consistency with existing literature on brain structure changes.
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.