An Efficient Method for Constructing Optimal Statistical Shape Models

Davies, Rhodri H.; Cootes, Timothy F.; Waterton, John C.; Taylor, Christopher J.

doi:10.1007/3-540-45468-3_8

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…A number of automated shape registration and model building methods have been proposed [7], [4], [5], [6]. These approaches either establish correspondences between geometric features, such as critical points of high curvature [5]; or find the "best" corresponding parametrization model by optimizing some criterion, such as minimizing accumulated Euclidean Distance [4], [6], Minimum Description Length [7], or Spline Bending Energy [6].…”

Section: Introductionmentioning

confidence: 99%

“…These approaches either establish correspondences between geometric features, such as critical points of high curvature [5]; or find the "best" corresponding parametrization model by optimizing some criterion, such as minimizing accumulated Euclidean Distance [4], [6], Minimum Description Length [7], or Spline Bending Energy [6]. Both geometric feature based and explicit parameterization based registration methods are not suitable for incorporating region intensity information.…”

Section: Introductionmentioning

confidence: 99%

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Learning Coupled Prior Shape and Appearance Models for Segmentation

Huang

Metaxas

2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract. We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape and intensity spaces are unified by implicitly representing shapes as "images" in the space of distance transforms. A stochastic chord-based matching algorithm is developed to align photo-realistic training examples under a common reference frame. Then dense local deformation fields, represented using the cubic B-spline based Free Form Deformations (FFD), are recovered to register the training examples in both shape and intensity spaces. Principal Component Analysis (PCA) is applied on the FFD control lattices to capture the variations in shape as well as on registered object interior textures. We show examples where we have built coupled shape and appearance prior models for the left ventricle and whole heart in short-axis cardiac tagged MR images, and used them to delineate the heart chambers in noisy, cluttered images. We also show quantitative validation on the automatic segmentation results by comparing to expert solutions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Coupled Prior Shape and Appearance Models for Segmentation

Huang

Metaxas

2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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“…One possibility to automate this is to use the parameterization of shapes, like spherical harmonics [3]. Davies et al found the point correspondence via an optimization problem [5]. Rueckert et al applied PCA to the deformation fields that were got by registering the volumes in a training set to a reference volume [6].…”

Section: Introductionmentioning

confidence: 99%

Model Library for Deformable Model-Based Segmentation of 3-D Brain MR-Images

Koikkalainen

Lötjönen

2002

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

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A novel method to use model libraries in segmentation is introduced. Using similarity measures one model from a model library is selected. This model is then used in model-based segmentation. The proposed method is simple, straightforward and fast. Various similarity measures, both voxel and edge measures, were examined. Two different segmentation methods were used for validating the functionality of the proposed procedure. Results show that a statistically significant improvement in segmentation accuracy was achieved in each study case.

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Aligning Shapes by Minimising the Description Length

Ericsson

Karlsson

2005

Image Analysis

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When building shape models, it is first necessary to filter out the similarity transformations from the original configurations. This is normally done using Procrustes analysis, that is minimising the sum of squared distances between the corresponding landmarks under similarity transformations. In this article we propose to align shapes using the Minimum Description Length (MDL) criterion. Previously MDL has been used to locate correspondences. We show that the Procrustes alignment with respect to rotation is not optimal.The MDL based algorithm is compared with Procrustes on a number of data sets. It is concluded that there is improvement in generalisation when using Minimum Description Length. With a synthetic example it is shown that the Procrustes alignment can fail significantly where the proposed method does not.The Description Length is minimised using Gauss-Newton. In order to do this the derivative of the description length with respect to rotation is derived.

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An Efficient Method for Constructing Optimal Statistical Shape Models

Cited by 10 publications

References 18 publications

Learning Coupled Prior Shape and Appearance Models for Segmentation

Learning Coupled Prior Shape and Appearance Models for Segmentation

Model Library for Deformable Model-Based Segmentation of 3-D Brain MR-Images

Aligning Shapes by Minimising the Description Length

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