2016/02/17 by Snehalata Huzurbazar, Huzurbazar, S., Long Lee +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fractal and DNA sequence analysis #Image Retrieval and Classification Techniques #Morphological variations and asymmetry
paper · pdf · doi:10.48550/arxiv.1602.05572
openalex publication_date 2016/02/17 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.