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Article
Open AccessChoiceNet: CNN learning through choice of multiple feature map representations
We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of v...
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Article
Open AccessAuthor Correction: Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Chapter and Conference Paper
Small Defect Detection Using Convolutional Neural Network Features and Random Forests
We address the problem of identifying small abnormalities in an imaged region, important in applications such as industrial inspection. The goal is to label the pixels corresponding to a defect with a minimum ...
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Article
Open AccessFully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms
Cephalometric tracing is a standard analysis tool for orthodontic diagnosis and treatment planning. The aim of this study was to develop and validate a fully automatic landmark annotation (FALA) system for fin...
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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
Segmentation of Lumbar Vertebrae Using Part-Based Graphs and Active Appearance Models
The aim of the work is to provide a fully automatic method of segmenting vertebrae in spinal radiographs. This is of clinical relevance to the diagnosis of osteoporosis by vertebral fracture assessment, and to...
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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
Shape Discrimination in the Hippocampus Using an MDL Model
We extend recent work on building 3D statistical shape models, automatically, from sets of training shapes and describe an application in shape analysis. Using an existing measure of model quality, based on a ...
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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,...
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Chapter and Conference Paper
An Efficient Method for Constructing Optimal Statistical Shape Models
Statistical shape models show considerable promise as a basis for segmenting and interpreting images. A major drawback of the approach is the need to establish a dense correspondence across a training set of s...
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Chapter and Conference Paper
A Minimum Description Length Approach to Statistical Shape Modelling
Statistical shape models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences betw...
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Chapter and Conference Paper
Locating Overlap** Flexible Shapes Using Geometrical Constraints
In an earlier paper [1] we have proposed a shape representation called the CLD (Chord Length Distribution) which possesses many of the often-quoted desirable properties of a shape representation. It also captu...
