Use of a probabilistic shape model for non-linear registration of 3D scattered data
نویسندگان
چکیده
In this paper we address the problem of registering 3D scattered data by the mean of a statistical shape model. This model is built from a training set on which a principal component analysis ( E A ) is applied. A local system of reference is computed for each sample shape of the learning set, what enables to align the training set. PCA then reveals the main modes of deformation of the class of objects of interest. Furthermore, the deformation field obtained between a given shape and a reference shape is extended to a local neighborhood of these shapes by using an interpolation based on the thin-plate splines. It is then used to register objects associated with these shapes in a local and non-linear way. The data involved here are cerebral data both anatomical (cortical sulci) and functional (MEG dipoles).
منابع مشابه
Probabilistic Linkage of Persian Record with Missing Data
Extended Abstract. When the comprehensive information about a topic is scattered among two or more data sets, using only one of those data sets would lead to information loss available in other data sets. Hence, it is necessary to integrate scattered information to a comprehensive unique data set. On the other hand, sometimes we are interested in recognition of duplications in a data set. The i...
متن کاملNovel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection
In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...
متن کاملDeformable Density Matching for 3D Non-rigid Registration of Shapes
There exists a large body of literature on shape matching and registration in medical image analysis. However, most of the previous work is focused on matching particular sets of features--point-sets, lines, curves and surfaces. In this work, we forsake specific geometric shape representations and instead seek probabilistic representations--specifically Gaussian mixture models--of shapes. We ev...
متن کاملA new approach to scatter correction in SPECT images based on Klein_Nishina equation
Introduction: Scattered photon is one of the main defects that degrade the quality and quantitative accuracy of nuclear medicine images. Accurate estimation of scatter in projection data of SPECT is computationally extremely demanding for activity distribution in uniform and non-uniform dense media. Methods: The objective of this paper is to develop and validate a scatter correction technique ...
متن کاملA Probabilistic Bayesian Classifier Approach for Breast Cancer Diagnosis and Prognosis
Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...
متن کامل