Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Functional Data Analysis with R and MATLAB
Functional Data Analysis with R and MATLAB
Performance evaluation of grid-enabled registration algorithms using bronze-standards
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
IEEE Transactions on Image Processing
Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy
Computer Methods and Programs in Biomedicine
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The registration of 2D and 3D images is one of the key tasks in medical image processing and analysis. Accurate registration is a crucial preprocessing step for many tasks; consequently, the evaluation of its accuracy becomes necessary. Unfortunately, this is a difficult task, especially when no golden pattern (true result) is available and when the signal values may have changed between successive images to be registered. This is the case this paper deals with: we have a series of 3D images, magnetic resonance images (MRI) of the liver and adjacent areas that have to be registered. They have been taken while a contrast is diffused through the liver tissue, so intensity of each observed point changes for two reasons: contrast diffusion/perfusion and deformation of the liver (due to body movement and breathing). In this paper, we introduce a new method to automatically compare two or more registration algorithms applied to the same case of a perfusion magnetic resonance dynamic image so that the best of them can be chosen when no ground truth is available. This is done by modeling the function that gives the intensity at a given point as a functional datum, and using statistical techniques to assess its change in comparison with other functions. An example of the application is shown by comparing two parametrizations of a B-spline based registration algorithm. The main result of the proposed method is a suggestive evidence to guide the physician in the process of selecting a registration algorithm, that recommends the algorithm of minimal complexity but still suitable for the case to be analyzed.