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Application of Varitation and Pde in3d-segmentation of Pulmonary CT Images

Author: LiMingChao
Tutor: DingXiaoHua
School: Harbin Institute of Technology
Course: Computational Mathematics
Keywords: 3D-segmentation CT MAP PDE double dimension reduction curvature flow
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 16
Quote: 0
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The use of chest computer tomography (CT) has strongly improved theradiologists’ detection rate of the lung cancer. In the last few years, the technologyof computer-aided detection (CAD) has drawn widespread attention since its highefficiency. As a part of CAD,the automatic segmentation of lung region in chest CTimage has always been a popular field, and it has great significant to the treatmentof cancer.In this paper, a novel CAD system for the automatic3D-segmentation of thelung in Chest computed tomography (CT) images is presented. The method is baesdon varitation and PDE. Firstly, an improved maximum a posteriori (MAP)estimation framework, combing neighbor prior information and image gray levelinformation, is used to extract the surface of lung from the chest CT images. Therelationship of the left lung and the right lung is represented as a joint densityfunction. A double dimension reduction algorithm is developed to build the neighborprior model in a set of training images. This algorithm is more effective and feasiblethan traditional methods in three-dimensional space. In order to get a betterconstraint, the principal components’ coefficients of the prior models are redesigned.The improved MAP model is formulated in terms of level set functions, and thesurfaces evolve according to the associated Euler-Lagrange equations.After the preliminary segmentation of lung surfaces, a new algorithm basedon the prior information and curvature flow will be proposed to refine the surfacegenerated by the improved MAP framework. This algorithm consists of two stages:1, automatically detecting and rough fitting the region of lung hilum with the priorinformation;2, refining the fitting surfaces based on PDE.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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