And Statistical Shape Deformation Model Abstract

Medical Image Registration Based on

Generalized Ndimensional Principal

Component Analysis (GND-PCA)

And Statistical Shape Deformation

Model

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Abstract:

This paper presents a fast and accurate image registration method for high dimensional images.

The method uses a statistical shape deformation model to represent deformation fields which warp an individual image to a selected template image. The statistical shape deformation model is built by the generalized N-dimensional principal component analysis (GND-PCA) with training samples of deformation fields, which deform the individual sample images to the selected template image. The statistical deformation model can be built with fewer samples and can represent individual deformation fields effectively by a small number of parameters, which is used to rapidly estimate the deformation field between the template image and a new individual image. The estimated deformation field is used to warp the individual image, and the warped image is close to the template image. The shape difference between the warped individual image and the template is estimated by an image registration algorithm, e.g., HAMMER. The proposed method has been validated by 3D MR brain images

.

Existing method:



Medical image registration is a key step in medical image analysis. Recently, the research focused on the nonrigid image registration, which has been extensively investigated in medical image analysis. The anatomical and functional variation within a group of individuals can be quantitatively evaluated by the deformation fields with respect to a selected template

Demerits:

1.

The information shows from normal process.

Proposed method:



A statistic based registration method is proposed for high-dimensional data. The proposed method needs to build the statistical shape deformation model with deformation fields, which are obtained by warping training high-dimensional data to a selected template. This model can represent the deformation field well with fewer parameters. When a new individual image is warped to the selected template, firstly the optimal parameters of the statistical shape deformation model are obtained by the optimization method, e.g., Levenberg Marquardt so as to get the optimal deformation field, which warps the new individual image to the selected template. The warped image is close to the selected template and is easy to register to the selected template .

Merits:

1.

The information shows from 1-D, 2-D and 3-D information.

Block diagram:



Fig. The overview of the proposed method.



And Statistical Shape Deformation Model Abstract

Medical Image Registration Based on

Generalized Ndimensional Principal

Component Analysis (GND-PCA)