scholarly journals Optimal Sub-Shape Models by Minimum Description Length

2005 ◽  
Author(s):  
G. Langs ◽  
P. Peloschek ◽  
H. Bischof
Keyword(s):  
Minimum Description Length ◽  
Shape Models

scholarly journals Particle System Based Adaptive Sampling on Spherical Parameter Space to Improve the MDL Method for Construction of Statistical Shape Models

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Rui Xu ◽  
Xiangrong Zhou ◽  
Yasushi Hirano ◽  
Rie Tachibana ◽  
Takeshi Hara ◽  
...  
Keyword(s):  
Unit Sphere ◽  
Adaptive Sampling ◽  
State Of The Art ◽  
Particle System ◽  
Minimum Description Length ◽  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape ◽  
Left And Right ◽  
3D Shapes

Minimum description length (MDL) based group-wise registration was a state-of-the-art method to determine the corresponding points of 3D shapes for the construction of statistical shape models (SSMs). However, it suffered from the problem that determined corresponding points did not uniformly spread on original shapes, since corresponding points were obtained by uniformly sampling the aligned shape on the parameterized space of unit sphere. We proposed a particle-system based method to obtain adaptive sampling positions on the unit sphere to resolve this problem. Here, a set of particles was placed on the unit sphere to construct a particle system whose energy was related to the distortions of parameterized meshes. By minimizing this energy, each particle was moved on the unit sphere. When the system became steady, particles were treated as vertices to build a spherical mesh, which was then relaxed to slightly adjust vertices to obtain optimal sampling-positions. We used 47 cases of (left and right) lungs and 50 cases of livers, (left and right) kidneys, and spleens for evaluations. Experiments showed that the proposed method was able to resolve the problem of the original MDL method, and the proposed method performed better in the generalization and specificity tests.

Comparison of two statistical shape models for the femur segmentation in MRI

10.29007/6wxx ◽  
2018 ◽  
Author(s):  
Zoheir Dib ◽  
Tinashe Mutsvangwa ◽  
Guillaume Dardenne ◽  
Chafiaa Hamitouche ◽  
Valérie Burdin ◽  
...  
Keyword(s):  
Minimum Description Length ◽  
Computation Time ◽  
Gaussian Mixture ◽  
Statistical Shape Models ◽  
Active Shape Models ◽  
Shape Models ◽  
Statistical Shape ◽  
Validation Technique ◽  
Leave One Out ◽  
The Impact

Active Shape Models (ASM) have been widely used in the literature for the extraction of the tibial and the femoral bones from MRI. These methods use Statistical Shape Models (SSM) to drive the deformation and make the segmentation more robust. One crucial step for building such SSM is the shape correspondence (SC). Several methods have been described in the literature. The goal of this paper is to compare two SC methods, the Iterative Median Closest Point-Gaussian Mixture Model (IMCP- GMM) and the Minimum Description Length (MDL) approaches for the creation of a SSM, and to assess the impact of these SC methods on the accuracy of the femur segmentation in MRI. 28 MRI of the knee have been used. The validation has been performed by using the leave-one-out cross-validation technique. An ASMMDL and an ASMIMCP-GMMM has been built with the SSMs computed respectively with the MDL and IMCP-GMM methods. The computation time for building both SSMs has been also measured. For 90% of data, the error is inferior to 1.78 mm and 1.85 mm for respectively the ASMIMCP-GMM and the ASMMDL methods. The computation time for building the SSMs is five hours and two days for respectively the IMCP-GMM and the MDL methods. Both methods seems to give, at least, similar results for the femur segmentation in MRI. However (1) IMCP-GMM can be used for all types of shape, this is not the case for the MDL method which only works for closed shape, and (2) IMCP- GMM is much faster than MDL.

Spectrum Sensing Algorithm Based on Minimum Description Length Criterion for Sparse Multiband Signal

2013 ◽  
Vol 34 (7) ◽  
pp. 1547-1551
Author(s):  
Peng Yang ◽  
Zheng Liu ◽  
Wen-li Jiang
Keyword(s):  
Spectrum Sensing ◽  
Minimum Description Length

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

scholarly journals Estimating Reference Bony Shape Models for Orthognathic Surgical Planning Using 3D Point-Cloud Deep Learning

2021 ◽  
pp. 1-1
Author(s):  
Deqiang Xiao ◽  
Chunfeng Lian ◽  
Han Deng ◽  
Tianshu Kuang ◽  
Qin Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Surgical Planning ◽  
3D Point Cloud ◽  
Shape Models

scholarly journals Cellcyclegan: Spatiotemporal Microscopy Image Synthesis Of Cell Populations Using Statistical Shape Models And Conditional Gans

2021 ◽  
Author(s):  
Dennis Bahr ◽  
Dennis Eschweiler ◽  
Anuk Bhattacharyya ◽  
Daniel Moreno-Andres ◽  
Wolfram Antonin ◽  
...  
Keyword(s):  
Image Synthesis ◽  
Cell Populations ◽  
Statistical Shape Models ◽  
Shape Models ◽  
Microscopy Image ◽  
Statistical Shape

scholarly journals Conditional Shape Models for Cardiac Motion Estimation

2010 ◽  
pp. 452-459 ◽  
Author(s):  
Coert Metz ◽  
Nora Baka ◽  
Hortense Kirisli ◽  
Michiel Schaap ◽  
Theo van Walsum ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Cardiac Motion ◽  
Shape Models

A framework for the merging of pre-existing and correspondenceless 3D statistical shape models

2014 ◽  
Vol 18 (7) ◽  
pp. 1044-1058 ◽  
Author(s):  
Marco Pereañez ◽  
Karim Lekadir ◽  
Constantine Butakoff ◽  
Corné Hoogendoorn ◽  
Alejandro F. Frangi
Keyword(s):  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape

scholarly journals Parametrized shape models for clothing

2011 ◽  
Author(s):  
Stephen Miller ◽  
Mario Fritz ◽  
Trevor Darrell ◽  
Pieter Abbeel
Keyword(s):  
Shape Models
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