Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification

Ramı́rez, Javier; Górriz, Juan Manuel; Segovia, F.; Chaves, R.; Salas-González, D.; López, Manuel López; Álvarez, Ignacio; Padilla, Pablo

doi:10.1016/j.neulet.2010.01.056

Cited by 117 publications

(64 citation statements)

References 15 publications

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“…For instance, the authors in (López et al., 2009) conducted a data reduction on features extracted from single photon emission computed tomography (SPECT) and positron emission tomography (PET) images by means of principal component analysis (PCA). In (Ramírez et al., 2010) a new data reduction method was introduced by the means of partial least squares (PLSs) to overcome the curse of dimensionality. The authors applied the proposed PLS‐based method on SPECT data and extracted the score features for an AD classification task.…”

Section: Literate Reviewmentioning

confidence: 99%

“…The authors applied the proposed PLS‐based method on SPECT data and extracted the score features for an AD classification task. The PLS‐based data reduction has been widely used in different neuroimaging studies (Chaves, Ramírez, Górriz, & Puntonet, 2012; Khedher, Ramírez, Górriz, Brahim, & Segovia, 2015; Ramírez et al., 2010; Segovia, Górriz, Ramírez, Salas‐González, & Álvarez, 2013). The researchers in (Liu, Zhang, & Shen, 2016) used a sparse‐based feature selection method to find informative features from template space for AD classification and MCI conversion prediction.…”

Section: Literate Reviewmentioning

confidence: 99%

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The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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IntroductionWe present the Brain‐Age Score (BAS) as a magnetic resonance imaging (MRI)‐based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans.MethodsWe trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable‐MCI (sMCI), 112 progressive‐MCI (pMCI), and 147 AD patients from the J‐ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini‐Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume.ResultsThe correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume).DiscussionThe BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.

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Section: Literate Reviewmentioning

confidence: 99%

Section: Literate Reviewmentioning

confidence: 99%

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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“…Colliot et al [19] reported that their developed method based on hippocampal volumes in 3D T1-weightedMR images achieved a classification rate of 84% (a sensitivity of 84%, a specificity of 84%, and a AUC value of 0.913) between 25 AD patients (mean age: 73; mean MMSE: 24) and 25 controls (mean age: 64; MMSE: no description). Ramirez et al [20] developed a CAD system for AD patients based on a baseline principal component analysis (PCA) system in brain SPECT images. They re- ported a sensitivity of 100%, a specificity of 92.7%, and an accuracy of 96.9% for 41 AD cases and 56 CN cases (age and MMSE were not mentioned).…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, various kinds of computer-aided diagnosis (CAD) methods for AD patients have been developed [17][18][19][20][21]. However, to the best of our knowledge, there is no CAD system for the classification of AD patients using machine learning with morphological and functional image features obtained by MR imaging alone.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

Yamashita¹,

Arimura²,

Yamada³

et al. 2013

JBiSE

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Alzheimer's disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (

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“…Numerous studies have reported methods for distinguishing patients with AD from healthy cases [5][6][7][8], but few studies have described methods for distinguishing patients with AD from other types of dementia [9]. To our knowledge, no study to date has described the classification of AD versus PD using SPECT imaging.…”

Section: Introductionmentioning

confidence: 99%

Classification of Alzheimer’s disease and Parkinson’s disease using a support vector machine and probabilistic outputs

Hatabu

Harada

Takahashi³

et al. 2017

CBIJ

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Alzheimer's disease (AD) and Parkinson's disease (PD) are both prominent central nervous system diseases that are frequently diagnosed and studied using brain single-photon emission computed tomography (SPECT). Owing to divergent clinical features, AD and PD are often considered distinct diseases; however, it is difficult to distinguish AD from PD on SPECT. Tools for objectively analyzing differences between AD and PD on SPECT images are not currently available. To construct a model for discriminating AD from PD in Japanese patients, we used a support vector machine (SVM) and SPECT images acquired at two different time points after radiotracer injection to extract the determinant regions for classification. We assessed SPECT images from 68 Japanese patients with AD or PD. After pre-processing noise voxels, a non-linear SVM classification with Gaussian kernels was adopted to construct the predictive model. The best SVM model was highly accurate for distinguishing AD from PD. The accuracy of this model was 98.1% for leave-one-out cross-validation and 78.6% for the test set. Our data showed that the temporal, sub-lobar, parietal, limbic, and frontal areas exhibited decreased regional cerebral blood flow in AD; whereas the frontal, anterior, parietal, and occipital areas exhibited decreased regional cerebral blood flow in PD. Here, we present a useful SVM model for classifying AD versus PD using SPECT images and show the utility of two-time-point SPECT imaging for AD/PD discrimination.

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Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification

Cited by 117 publications

References 15 publications

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

Classification of Alzheimer’s disease and Parkinson’s disease using a support vector machine and probabilistic outputs

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