Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration
Publication year
2012Source
Medical Image Analysis, 16, (2012), pp. 1521–1531ISSN
Publication type
Article / Letter to editor
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Organization
Radiology
Journal title
Medical Image Analysis
Volume
vol. 16
Page start
p. 1521–1531
Page end
p. 1521–1531
Subject
N4i 3: Poverty-related infectious diseases ONCOL 5: Aetiology, screening and detection; Medical Imaging - Radboud University Medical CenterAbstract
A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal multi-feature model, classifies local alignments into three quality categories: correct, poor or wrong alignment. We establish a reference registration error set as basis for training and testing of the method. It consists of image registrations obtained from different nonrigid registration algorithms and manually established point correspondences of automatically determined landmarks. We employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single-feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi-feature model is validated against the set of single-feature classifiers. We consider the setup generic, however, its application is demonstrated on 51 CT follow-up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%.
This item appears in the following Collection(s)
- Academic publications [241901]
- Electronic publications [127360]
- Faculty of Medical Sciences [91954]
- Open Access publications [102119]
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