Intensity normalization of DaTSCAN SPECT imaging using a model-based clustering approach

Brahim, Abdelbasset; Górriz, J. M.; Ramı́rez, Javier; Khedher, L.

doi:10.1016/j.asoc.2015.08.030

Cited by 17 publications

(9 citation statements)

References 42 publications

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“…Step 1 : A mask is applied to source images in order to consider only voxels in the brain outside the striatum (Brahim et al, 2015 ). This will reduce the computational load without losing too much accuracy.…”

Section: Methodsmentioning

confidence: 99%

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

Castillo-Barnes

Ramı́rez

Segovia

et al. 2018

Front. Neuroinform.

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In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.

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Section: Methodsmentioning

confidence: 99%

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

Castillo-Barnes

Ramı́rez

Segovia

et al. 2018

Front. Neuroinform.

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“…However, in these works, the authors only included images from a single center and the multisite problem was not explicitly tackled. Moreover, our method has yielded higher classification performances [15][16][17].…”

Section: Discussionmentioning

confidence: 99%

“…This could maximize objectivity, practicability, and reproducibility against traditional specialist-dependent calculations. Indeed, there have been other recent proposals to intensity normalize DAT SPECT images with voxel-based methods [15][16][17]. However, in these works, the authors only included images from a single center and the multisite problem was not explicitly tackled.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models

et al. 2018

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Purpose: Differences in site, device, and/or settings may cause large variations in the intensity profile of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images. However, the current standard to evaluate these images, the striatal binding ratio (SBR), does not efficiently account for this heterogeneity and the assessment can be unequivalent across distinct acquisition pipelines. In this work, we present a voxel-based automated approach to intensity normalize such type of data that improves on cross-session interpretation. Procedures: The normalization method consists of a reparametrization of the voxel values based on the cumulative density function (CDF) of a Gamma distribution modeling the specific region intensity. The harmonization ability was tested in 1342 SPECT images from the PPMI repository, acquired with 7 distinct gamma camera models and at 24 different sites. We compared the striatal quantification across distinct cameras for raw intensities, SBR values, and after applying the Gamma CDF (GDCF) harmonization. As a proof-of-concept, we evaluated the impact of GCDF normalization in a classification task between controls and Parkinson disease patients. Results: Raw striatal intensities and SBR values presented significant differences across distinct camera models. We demonstrate that GCDF normalization efficiently alleviated these differences in striatal quantification and with values constrained to a fixed interval [0,1]. Also, our method allowed a fully automated image assessment that provided maximal classification ability, given by an area under the curve (AUC) of AUC = 0.94 when used mean regional variables and AUC = 0.98 when used voxel-based variables. Conclusion: The GCDF normalization method is useful to standardize the intensity of DAT SPECT images in an automated fashion and enables the development of unbiased algorithms using multicenter datasets. This method may constitute a key pre-processing step in the analysis of this type of images.Alberto Llera and Ismael Huertas shared first authorship. Electronic supplementary material The online version of this article (https://

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“…Magnetic Resonance Imaging (MRI) 1 is commonly used to check the brain status of patients in most Chinese hospitals, since for the soft tissue structures it can perform better than other imaging modalities in terms of clarity and the aspect of extraction details, such as computed tomography (CT), 2 single-photon emission CT (SPECT), 3 magnetic resonance spectrum imaging (MRSI), 4 x-ray, 5 etc. As reported, manual classification of magnetic resonance (MR) brain images is tedious, time consuming, costly, and irreproducible.…”

Section: Introductionmentioning

confidence: 99%

Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

et al. 2016

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Finding an appropriate and accurate technology for early detection of disease is significantly important to research early treatments. We proposed some novel automatic classification systems based on the stationary wavelet transform (SWT) and the improved support vector machine (SVM). Magnetic Resonance Imaging (MRI) is commonly used for brain imaging as a non-invasive diagnostic tool to assist the pre-clinical diagnosis. However, MRI generates a large information set, which poses a challenge for classification. To deal with this problem we proposed a new approach, which combines SWT and Principal Component Analysis for feature extraction. In our experiments, three different datasets and four kinds of classifiers of the SVM were employed. The results over 5×6-fold stratified cross-validation (SCV) for Dataset-66, and 5×5-fold SCV for the other two datasets show that the average accuracy is almost 100.00%.

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Intensity normalization of DaTSCAN SPECT imaging using a model-based clustering approach

Cited by 17 publications

References 42 publications

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models

Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

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