7 Result(s)
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Chapter and Conference Paper
Learning-Based Shape Model Matching: Training Accurate Models with Minimal Manual Input
Recent work has shown that statistical model-based methods lead to accurate and robust results when applied to the segmentation of bone shapes from radiographs. To achieve good performance, model-based matchin...
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Chapter and Conference Paper
Accurate Bone Segmentation in 2D Radiographs Using Fully Automatic Shape Model Matching Based On Regression-Voting
Recent work has shown that using Random Forests (RFs) to vote for the optimal position of model feature points leads to robust and accurate shape model matching. This paper applies RF regression-voting as part...
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Chapter and Conference Paper
Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting
A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regre...
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Chapter and Conference Paper
Deformable Object Modelling and Matching
Statistical models of the shape and appearance of deformable objects have become widely used in Computer Vision and Medical Image Analysis. Here we give an overview of such models and of two efficient algorith...
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Chapter and Conference Paper
3D Brain Segmentation Using Active Appearance Models and Local Regressors
We describe an efficient and accurate method for segmenting sets of subcortical structures in 3D MR images of the brain. We first find the approximate position of all the structures using a global Active Appea...
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Chapter and Conference Paper
Comparison and Evaluation of Segmentation Techniques for Subcortical Structures in Brain MRI
The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of ...
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Chapter and Conference Paper
3D Statistical Shape Models Using Direct Optimisation of Description Length
We describe an automatic method for building optimal 3D statistical shape models from sets of training shapes. Although shape models show considerable promise as a basis for segmenting and interpreting images,...
