A Minimum Description Length Approach to Statistical Shape Modelling

Davies, Rhodri H.; Cootes, T. F.; Taylor, Chris

doi:10.1007/3-540-45729-1_5

Cited by 75 publications

(90 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Sun et al [15], the ardiac dynamics is learnt by using a second order dynamic model. Davies et al [16] propose a method to automatically extract a set of optimal landmarks using the minimum description length (MDL). But it is not clear whether the landmarks thus extracted are optimal in the sense of anatomical correspondence.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

View full text Add to dashboard Cite

In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

View full text Add to dashboard Cite

show abstract

“…This was developed further by Cootes et al [35] who modelled both 2D shape and appearance in their widely used Active Appearance Model framework. More recently, groupwise alignment has been used to automatically register samples of images using both stochastic [83] and minimum description length [84] approaches. In 3D, Blanz and Vetter [25] used a version of optical flow applied to cylindrical parameterisations of the face surfaces to establish dense correspondence between each face and a chosen template face (i.e.…”

Section: Statistical Face Modellingmentioning

confidence: 99%

Inverse Rendering of Faces with a 3D Morphable Model

Aldrian

Smith

2013

IEEE Trans. Pattern Anal. Mach. Intell.

176

160

View full text Add to dashboard Cite

In this thesis, we present a complete framework to inverse render faces with a 3D Morphable Model. By decomposing the image formation process into a geometric and photometric part, we are able to state the problem as a multilinear system which can be solved accurately and efficiently. As we treat each contribution as independent, the objective function is convex in the parameters and a globally optimal solution can be found. We start by recovering 3D shape using a novel algorithm which incorporates generalisation errors of the model obtained from empirical measurements. The algorithm is extended so it can efficiently deal with mixture distributions. We then describe three methods to recover facial texture, and for the second and third, diffuse lighting, specular reflectance and camera properties from a single image. These methods make increasingly weak assumptions and can all be solved in a linear fashion. We further modify our framework so it accounts for global illumination effects. This is achieved by incorporating statistical models for ambient occlusion and bent normals into the image formation model. We show that solving for ambient occlusion and bent normal parameters as part of the fitting process improves the accuracy of the estimated texture map and illumination environment. We present results on challenging data, rendered under complex natural illumination with both specular reflectance and occlusion of the illumination environment. We evaluate our findings on publicly available datasets, where we are able to obtain state-ofthe-art results. Finally, we present a practical method to synthesise a larger population from a small training-set and show how the new instances can be used to build a flexible PCA model.

show abstract

“…In [19], the cardiac dynamics is learnt by using a second-order dynamic model. Davies et al in [20] propose a method to automatically extract a set of optimal landmarks using the minimum description length. But it is not clear whether the landmarks thus extracted are optimal in the sense of anatomical correspondence.…”

Section: Knowledge-based Cardiac Segmentationmentioning

confidence: 99%

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Mahapatra

2013

J Digit Imaging

View full text Add to dashboard Cite

In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty" that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentations.

show abstract

A Minimum Description Length Approach to Statistical Shape Modelling

Cited by 75 publications

References 14 publications

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Inverse Rendering of Faces with a 3D Morphable Model

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Contact Info

Product

Resources

About