Share Email Print
cover

Proceedings Paper

Sparse modeling of landmark and texture variability using the orthomax criterion
Author(s): Mikkel B. Stegmann; Karl Sjöstrand; Rasmus Larsen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In the past decade, statistical shape modeling has been widely popularized in the medical image analysis community. Predominantly, principal component analysis (PCA) has been employed to model biological shape variability. Here, a reparameterization with orthogonal basis vectors is obtained such that the variance of the input data is maximized. This property drives models toward global shape deformations and has been highly successful in fitting shape models to new images. However, recent literature has indicated that this uncorrelated basis may be suboptimal for exploratory analyses and disease characterization. This paper explores the orthomax class of statistical methods for transforming variable loadings into a simple structure which is more easily interpreted by favoring sparsity. Further, we introduce these transformations into a particular framework traditionally based on PCA; the Active Appearance Models (AAMs). We note that the orthomax transformations are independent of domain dimensionality (2D/3D etc.) and spatial structure. Decompositions of both shape and texture models are carried out. Further, the issue of component ordering is treated by establishing a set of relevant criteria. Experimental results are given on chest radiographs, magnetic resonance images of the brain, and face images. Since pathologies are typically spatially localized, either with respect to shape or texture, we anticipate many medical applications where sparse parameterizations are preferable to the conventional global PCA approach.

Paper Details

Date Published: 10 March 2006
PDF: 12 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441G (10 March 2006); doi: 10.1117/12.651293
Show Author Affiliations
Mikkel B. Stegmann, Technical Univ. of Denmark (Denmark)
Karl Sjöstrand, Technical Univ. of Denmark (Denmark)
Rasmus Larsen, Technical Univ. of Denmark (Denmark)


Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

© SPIE. Terms of Use
Back to Top