FEATURE-BASED VS. INTENSITY-BASED BRAIN IMAGE REGISTRATION: COMPREHENSIVE COMPARISON USING MUTUAL INFORMATION

We propose a mutual information-based method for quantitative evaluation of the deformable registration algorithms at three levels: global, voxel-wise and anatomical structure. We compare two fully deformable registration algorithms: feature-based HAMMER and a set of intensity-based algorithms (FEM-...

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Hauptverfasser: Teverovskiy, L.A., Carmichael, O.T., Aizenstein, H.J., Lazar, N., Liu, Y.
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Carmichael, O.T.
Aizenstein, H.J.
Lazar, N.
Liu, Y.
description We propose a mutual information-based method for quantitative evaluation of the deformable registration algorithms at three levels: global, voxel-wise and anatomical structure. We compare two fully deformable registration algorithms: feature-based HAMMER and a set of intensity-based algorithms (FEM-Demons) in the ITK package. Evaluation is carried out using the AAE template image with 116 labeled anatomical structures and a set of 59 MR brain images: 20 normal controls (CTE), 20 Alzheimer's disease patients (AD) and 19 mild cognitive impairment patients (MCI). We show that both HAMMER and FEM-Demons perform significantly better than an affine registration algorithm, FLIRT, at all three levels. At the global level, FEM-Demons outperforms HAMMER on the images of AD and MCI patients. At the local and anatomical levels, FEM-Demons and HAMMER dominate each other on different brain regions.
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Anatomical structure
Biomedical imaging
Brain
Deformable models
Entropy
Finite element methods
Image registration
Image segmentation
Mutual information
Packaging
title FEATURE-BASED VS. INTENSITY-BASED BRAIN IMAGE REGISTRATION: COMPREHENSIVE COMPARISON USING MUTUAL INFORMATION
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