Principal geodesic analysis for the study of nonlinear minimum description length

Su, Zihua, Lambrou, Tryphon and Todd-Pokropek, Andrew (2008) Principal geodesic analysis for the study of nonlinear minimum description length. Medical Imaging and Informatics: Lecture Notes in Computer Science, 4987 L . pp. 89-98. ISSN 0302-9743

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The essential goal for Statistical Shape Model (SSM) is to describe and extract the shape variations from the landmarks cloud. A standard technique for such variation extraction is by using Principal Component Analysis (PCA). However, PCA assumes that variations are linear in Euclidean vector space, which is not true or insufficient on many medical data. Therefore, we developed a new Geodesic Active Shape (GAS) mode by using Principal Geodesic Analysis (PGA) as an alternative of PCA. The new GAS model is combined with Minimum Description Length approach to find correspondence points across datasets automatically. The results are compared between original MDL and our proposed GAS MDL approach by using the measure of Specificity. Our preliminary results showed that our proposed GAS model achieved better scores on both datasets. Therefore, we conclude that our GAS model can capture shape variations reasonably more specifically than the original Active Shape Model (ASM). Further, analysis on the study of facial profiles dataset showed that our GAS model did not encounter the so-called "Pile Up" problem, whereas original MDL did. © 2008 Springer-Verlag Berlin Heidelberg.

Additional Information:Conference of 2nd International Conference on Medical Imaging and Informatics, MIMI 2007; Conference Date: 14 August 2007 through 16 August 2007; Conference Code: 7236
Keywords:Administrative data processing, Extraction, Face recognition, Financial data processing, Gas dynamics, Gases, Geodesy, Magnetosphere, Medical imaging, Metal working shapers, Modal analysis, Neodymium, Solvent extraction, Standards, Active shape, Active shape model (ASM), Data sets, Data-sets, Euclidean, Facial profiles, Heidelberg (CO), Informatics, international conferences, Medical data, Minimum Description Length (MDL), Minimum description length approach, Pile ups, Principal component analysis (PCA), Principal geodesic analysis (PGA), Shape variations, Springer (CO), Statistical shape modelling, Vector spaces, Principal component analysis, bmjtype, bmjdate
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:8670
Deposited On:24 Apr 2013 12:39

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