Improved Gauss-Newton optimisation methods in affine registration of SPECT brain images

Salas-González, D.; Górriz, J. M.; Ramírez, J.; Lass, A.; Puntonet, Carlos G.

doi:10.1049/el:20081838

Cited by 58 publications

(15 citation statements)

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“…Images of the brain cross-sections are reconstructed from the projection data using the filtered back-projection (FBP) algorithm in combination with a manually fixed Butterworth noise removal filter, giving rise to 2.18 Â 2.18 Â 3.56 voxels/mm 3 resolution brain images. The preprocessing and spatial normalisation procedure, described in detail in [6], is achieved using affine and nonlinear spatial normalisation [7], and essentially guarantees meaningful voxel-wise comparisons between images. The images were initially labelled by experienced physicians of the 'Virgen de las Nieves' hospital (Granada, Spain), within two classes: NORMAL and AD.…”

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Alzheimer's diagnosis using eigenbrains and support vector machines

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Alzheimer's diagnosis using eigenbrains and support vector machines

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“…Therefore, this normalization method is suitable for preprocessing of brain FP-CIT SPECT images for diagnosis purposes (where there are NC and PD images). Previously, the images have been spatially normalized to a common template using a general affine model with 12 parameters (Salas-Gonzalez et al, 2008).…”

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Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution

et al. 2013

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“…Following the scan, each image was reviewed for possible artifacts at the University of Michigan and all raw and processed study data was archived. Subsequently, the images were normalized through a general affine model, with 12 parameters [27] using the SPM5 software. After the affine normalization, the resulting image was registered using a more complex non-rigid spatial transformation model.…”

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Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

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et al. 2012

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BackgroundFunctional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer’s Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems.MethodsIt is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared.ResultsSeveral experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods.ConclusionsAll the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

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Improved Gauss-Newton optimisation methods in affine registration of SPECT brain images

Cited by 58 publications

References 4 publications

Alzheimer's diagnosis using eigenbrains and support vector machines

Alzheimer's diagnosis using eigenbrains and support vector machines

Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution

Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

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