A Noise Robust Statistical Texture Model

2003

Medical Image Analysis

Self Cite

The contribution of this paper is the adaptation of data driven methods for nonEuclidean metric decomposition of tangent space shape coordinates. The basic idea is to extend principal component analysis (PCA) to take into account the noise variance at different landmarks and at different shapes. We show examples where these non-Euclidean metric methods allow for easier interpretation by decomposition into meaningful modes of variation. The extensions to PCA are based on adaptation of maximum autocorrelation factors and the minimum noise fraction transform to shape decomposition. A common basis of the methods applied is the assessment of the annotation noise variance at individual landmarks. These assessments are based on local models or repeated annotations by independent operators. We show that the Molgedey-Schuster independent component analysis is equivalent to the maximum autocorrelation factors. Finally, the different subspace methods are compared using a probabilistic formulation based on their ability to represent the data.

Section: Introductionmentioning

confidence: 99%

Statistical shape analysis using non-Euclidean metrics

2003

Medical Image Analysis

Self Cite

“…For image data the noise process covariance is conveniently estimated using spatial filtering. In [5] the MNF transform is applied to texture modelling in active appearance models [6], and in [7] to multivariate images in extracting a discriminatory representation. Bookstein proposed using bending energy and inverse bending energy as metrics in the tangent space [8].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Generative Modelling

2003

Image Analysis

Self Cite

“…For image data the noise process covariance is conveniently estimated using spatial filtering. In [5] the MNF transform is applied to texture modelling in active appearance models [6]. Bookstein proposed using bending energy and inverse bending energy as metrics in the tangent space [7].…”

Section: Introductionmentioning

confidence: 99%

Statistical 2D and 3D Shape Analysis Using Non-Euclidean Metrics

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

Wrobel

2002

Self Cite

Abstract. The contribution of this paper is the adaptation of data driven methods for non-Euclidean metric decomposition of tangent space shape coordinates. This basic idea is to take extend principal components analysis to take into account the noise variance at different landmarks and at different shapes. We show examples where these non-Euclidean metric methods allow for easier interpretation by decomposition into biologically meaningful modes of variation. The extensions to PCA are based on adaptation of maximum autocorrelation factors and the minimum noise fraction transform to shape decomposition. A common basis of the methods applied is the assessment of the annotation noise variance at individual landmarks. These assessments are based on local models or repeated annotations by independent operators.