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A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051894] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.
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2
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Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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3
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Castillo-Barnes D, Ramírez J, Segovia F, Martínez-Murcia FJ, Salas-Gonzalez D, Górriz JM. Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease. Front Neuroinform 2018; 12:53. [PMID: 30154711 PMCID: PMC6102321 DOI: 10.3389/fninf.2018.00053] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 07/25/2018] [Indexed: 12/14/2022] Open
Abstract
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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Affiliation(s)
- Diego Castillo-Barnes
- Signal Processing and Biomedical Applications (SiPBA), Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramírez
- Signal Processing and Biomedical Applications (SiPBA), Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
| | - Fermín Segovia
- Signal Processing and Biomedical Applications (SiPBA), Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martínez-Murcia
- Signal Processing and Biomedical Applications (SiPBA), Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
| | - Diego Salas-Gonzalez
- Signal Processing and Biomedical Applications (SiPBA), Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
| | - Juan M Górriz
- Signal Processing and Biomedical Applications (SiPBA), Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
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4
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Segovia F, Sánchez-Vañó R, Górriz JM, Ramírez J, Sopena-Novales P, Testart Dardel N, Rodríguez-Fernández A, Gómez-Río M. Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages. Front Aging Neurosci 2018; 10:158. [PMID: 29930505 PMCID: PMC6001114 DOI: 10.3389/fnagi.2018.00158] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 05/08/2018] [Indexed: 01/17/2023] Open
Abstract
18F-FBB PET is a neuroimaging modality that is been increasingly used to assess brain amyloid deposits in potential patients with Alzheimer's disease (AD). In this work, we analyze the usefulness of these data to distinguish between AD and non-AD patients. A dataset with 18F-FBB PET brain images from 94 subjects diagnosed with AD and other disorders was evaluated by means of multiple analyses based on t-test, ANOVA, Fisher Discriminant Analysis and Support Vector Machine (SVM) classification. In addition, we propose to calculate amyloid standardized uptake values (SUVs) using only gray-matter voxels, which can be estimated using Computed Tomography (CT) images. This approach allows assessing potential brain amyloid deposits along with the gray matter loss and takes advantage of the structural information provided by most of the scanners used for PET examination, which allow simultaneous PET and CT data acquisition. The results obtained in this work suggest that SUVs calculated according to the proposed method allow AD and non-AD subjects to be more accurately differentiated than using SUVs calculated with standard approaches.
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Affiliation(s)
- Fermín Segovia
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Raquel Sánchez-Vañó
- Department of Nuclear Medicine, "9 de Octubre" Hospital, Valencia, Spain.,Clinical Medicine and Public Health Doctoral Program of the University of Granada, Granada, Spain
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.,Biosanitary Investigation Institute of Granada, Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.,Biosanitary Investigation Institute of Granada, Granada, Spain
| | | | - Nathalie Testart Dardel
- Department of Nuclear Medicine, "Virgen de las Nieves" University Hospital, Granada, Spain.,Department of Nuclear Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Antonio Rodríguez-Fernández
- Biosanitary Investigation Institute of Granada, Granada, Spain.,Department of Nuclear Medicine, "Virgen de las Nieves" University Hospital, Granada, Spain
| | - Manuel Gómez-Río
- Biosanitary Investigation Institute of Granada, Granada, Spain.,Department of Nuclear Medicine, "Virgen de las Nieves" University Hospital, Granada, Spain
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5
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Martinez-Murcia FJ, Górriz JM, Ramírez J, Segovia F, Salas-Gonzalez D, Castillo-Barnes D, Ortiz A. Assessing Mild Cognitive Impairment Progression using a Spherical Brain Mapping of Magnetic Resonance Imaging. J Alzheimers Dis 2018; 65:713-729. [PMID: 29630547 DOI: 10.3233/jad-170403] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Juan Manuel Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Spain.,Department of Psychiatry, University of Cambridge, UK
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Spain
| | - Fermín Segovia
- Department of Signal Theory, Networking and Communications, University of Granada, Spain
| | - Diego Salas-Gonzalez
- Department of Signal Theory, Networking and Communications, University of Granada, Spain
| | - Diego Castillo-Barnes
- Department of Signal Theory, Networking and Communications, University of Granada, Spain
| | - Andrés Ortiz
- Department of Communications Engineering, University of Malaga, Spain
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6
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Segovia F, Górriz JM, Ramírez J, Martínez-Murcia FJ, Salas-Gonzalez D. Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution. Front Aging Neurosci 2017; 9:326. [PMID: 29062277 PMCID: PMC5640782 DOI: 10.3389/fnagi.2017.00326] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Accepted: 09/20/2017] [Indexed: 11/16/2022] Open
Abstract
18F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson's disease (PD) that allows us to examine postsynaptic dopamine D2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of 18F-DMFP-PET images is concentrated in the striatum. However, other regions can also be useful for diagnostic purposes. An appropriate delimitation of the regions of interest contained in 18F-DMFP-PET data is crucial to improve the automatic diagnosis of PD. In this manuscript we propose a novel methodology to preprocess 18F-DMFP-PET data that improves the accuracy of computer aided diagnosis systems for PD. First, the data were segmented using an algorithm based on Hidden Markov Random Field. As a result, each neuroimage was divided into 4 maps according to the intensity and the neighborhood of the voxels. The maps were then individually normalized so that the shape of their histograms could be modeled by a Gaussian distribution with equal parameters for all the neuroimages. This approach was evaluated using a dataset with neuroimaging data from 87 parkinsonian patients. After these preprocessing steps, a Support Vector Machine classifier was used to separate idiopathic and non-idiopathic PD. Data preprocessed by the proposed method provided higher accuracy results than the ones preprocessed with previous approaches.
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Affiliation(s)
- Fermín Segovia
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.,Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | | | - Diego Salas-Gonzalez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
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7
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8
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Ortiz-Rosario A, Adeli H, Buford JA. MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates. Behav Brain Res 2016; 317:226-236. [PMID: 27650101 DOI: 10.1016/j.bbr.2016.09.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 09/07/2016] [Accepted: 09/11/2016] [Indexed: 01/07/2023]
Abstract
Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.
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Affiliation(s)
| | - Hojjat Adeli
- Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, Neurology, and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States.
| | - John A Buford
- Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, 453 W 10th Ave, Rm. 516E, Columbus, OH 43210, United States
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9
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Intensity normalization of DaTSCAN SPECT imaging using a model-based clustering approach. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.08.030] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Brahim A, Ramírez J, Górriz JM, Khedher L, Salas-Gonzalez D. Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism. PLoS One 2015; 10:e0130274. [PMID: 26086379 PMCID: PMC4473267 DOI: 10.1371/journal.pone.0130274] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 05/19/2015] [Indexed: 11/18/2022] Open
Abstract
Intensity normalization is an important pre-processing step in the study and analysis of DaTSCAN SPECT imaging. As most automatic supervised image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. In this work, a comparison between different novel intensity normalization methods is presented. These proposed methodologies are based on Gaussian Mixture Model (GMM) image filtering and mean-squared error (MSE) optimization. The GMM-based image filtering method is achieved according to a probability threshold that removes the clusters whose likelihood are negligible in the non-specific regions. The MSE optimization method consists of a linear transformation that is obtained by minimizing the MSE in the non-specific region between the intensity normalized image and the template. The proposed intensity normalization methods are compared to: i) a standard approach based on the specific-to-non-specific binding ratio that is widely used, and ii) a linear approach based on the α-stable distribution. This comparison is performed on a DaTSCAN image database comprising analysis and classification stages for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome (PS) detection. In addition, these proposed methods correct spatially varying artifacts that modulate the intensity of the images. Finally, using the leave-one-out cross-validation technique over these two approaches, the system achieves results up to a 92.91% of accuracy, 94.64% of sensitivity and 92.65 % of specificity, outperforming previous approaches based on a standard and a linear approach, which are used as a reference. The use of advanced intensity normalization techniques, such as the GMM-based image filtering and the MSE optimization improves the diagnosis of PS.
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Affiliation(s)
- A. Brahim
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - J. Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - J. M. Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - L. Khedher
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - D. Salas-Gonzalez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
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11
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A hybrid intelligent diagnosis approach for quick screening of Alzheimer's disease based on multiple neuropsychological rating scales. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:258761. [PMID: 25815043 PMCID: PMC4359840 DOI: 10.1155/2015/258761] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 11/20/2014] [Accepted: 11/20/2014] [Indexed: 01/23/2023]
Abstract
Neuropsychological testing is an effective means for the screening of Alzheimer's disease. Multiple neuropsychological rating scales should be used together to get subjects' comprehensive cognitive state due to the limitation of a single scale, but it is difficult to operate in primary clinical settings because of the inadequacy of time and qualified clinicians. Aiming at identifying AD's stages more accurately and conveniently in screening, we proposed a computer-aided diagnosis approach based on critical items extracted from multiple neuropsychological scales. The proposed hybrid intelligent approach combines the strengths of rough sets, genetic algorithm, and Bayesian network. There are two stages: one is attributes reduction technique based on rough sets and genetic algorithm, which can find out the most discriminative items for AD diagnosis in scales; the other is uncertain reasoning technique based on Bayesian network, which can forecast the probability of suffering from AD. The experimental data set consists of 500 cases collected by a top hospital in China and each case is determined by the expert panel. The results showed that the proposed approach could not only reduce items drastically with the same classification precision, but also perform better on identifying different stages of AD comparing with other existing scales.
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12
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Early diagnosis of Alzheimer׳s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.09.072] [Citation(s) in RCA: 175] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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13
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Segovia F, Holt R, Spencer M, Górriz JM, Ramírez J, Puntonet CG, Phillips C, Chura L, Baron-Cohen S, Suckling J. Identifying endophenotypes of autism: a multivariate approach. Front Comput Neurosci 2014; 8:60. [PMID: 24936183 PMCID: PMC4047979 DOI: 10.3389/fncom.2014.00060] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Accepted: 05/19/2014] [Indexed: 02/02/2023] Open
Abstract
The existence of an endophenotype of autism spectrum condition (ASC) has been recently suggested by several commentators. It can be estimated by finding differences between controls and people with ASC that are also present when comparing controls and the unaffected siblings of ASC individuals. In this work, we used a multivariate methodology applied on magnetic resonance images to look for such differences. The proposed procedure consists of combining a searchlight approach and a support vector machine classifier to identify the differences between three groups of participants in pairwise comparisons: controls, people with ASC and their unaffected siblings. Then we compared those differences selecting spatially collocated as candidate endophenotypes of ASC.
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Affiliation(s)
- Fermín Segovia
- Cyclotron Research Centre, University of LiègeLiège, Belgium
- Department of Psychiatry, Autism Research Centre, University of CambridgeCambridge, UK
| | - Rosemary Holt
- Department of Psychiatry, Autism Research Centre, University of CambridgeCambridge, UK
| | - Michael Spencer
- Department of Psychiatry, Autism Research Centre, University of CambridgeCambridge, UK
| | - Juan M. Górriz
- Department of Signal Theory, Networking and Communications, University of GranadaGranada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of GranadaGranada, Spain
| | - Carlos G. Puntonet
- Department of Computer Architecture and Technology, University of GranadaGranada, Spain
| | | | - Lindsay Chura
- Department of Psychiatry, Autism Research Centre, University of CambridgeCambridge, UK
| | - Simon Baron-Cohen
- Department of Psychiatry, Autism Research Centre, University of CambridgeCambridge, UK
| | - John Suckling
- Department of Psychiatry, Autism Research Centre, University of CambridgeCambridge, UK
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14
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Martínez-Murcia F, Górriz J, Ramírez J, Puntonet C, Illán I. Functional activity maps based on significance measures and Independent Component Analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:255-268. [PMID: 23660005 PMCID: PMC6701938 DOI: 10.1016/j.cmpb.2013.03.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2012] [Revised: 10/18/2012] [Accepted: 03/22/2013] [Indexed: 06/02/2023]
Abstract
The use of functional imaging has been proven very helpful for the process of diagnosis of neurodegenerative diseases, such as Alzheimer's Disease (AD). In many cases, the analysis of these images is performed by manual reorientation and visual interpretation. Therefore, new statistical techniques to perform a more quantitative analysis are needed. In this work, a new statistical approximation to the analysis of functional images, based on significance measures and Independent Component Analysis (ICA) is presented. After the images preprocessing, voxels that allow better separation of the two classes are extracted, using significance measures such as the Mann-Whitney-Wilcoxon U-Test (MWW) and Relative Entropy (RE). After this feature selection step, the voxels vector is modelled by means of ICA, extracting a few independent components which will be used as an input to the classifier. Naive Bayes and Support Vector Machine (SVM) classifiers are used in this work. The proposed system has been applied to two different databases. A 96-subjects Single Photon Emission Computed Tomography (SPECT) database from the "Virgen de las Nieves" Hospital in Granada, Spain, and a 196-subjects Positron Emission Tomography (PET) database from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Values of accuracy up to 96.9% and 91.3% for SPECT and PET databases are achieved by the proposed system, which has yielded many benefits over methods proposed on recent works.
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Affiliation(s)
- F.J. Martínez-Murcia
- Department of Signal Theory, Networking and Communications, 18071 University of Granada, Spain
| | - J.M. Górriz
- Department of Signal Theory, Networking and Communications, 18071 University of Granada, Spain
| | - J. Ramírez
- Department of Signal Theory, Networking and Communications, 18071 University of Granada, Spain
| | - C.G. Puntonet
- Department of Computer’s Architecture and Technology, 18071 University of Granada, Spain
| | - I.A. Illán
- Department of Signal Theory, Networking and Communications, 18071 University of Granada, Spain
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15
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Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution. Comput Biol Med 2013; 43:559-67. [PMID: 23485201 DOI: 10.1016/j.compbiomed.2013.01.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 09/27/2012] [Accepted: 01/07/2013] [Indexed: 11/20/2022]
Abstract
This work presents a study of the distribution of the grey matter (GM) and white matter (WM) in brain magnetic resonance imaging (MRI). The distribution of GM and WM is characterized using a mixture of α-stable distributions. A Bayesian α-stable mixture model for histogram data is presented and unknown parameters are sampled using the Metropolis-Hastings algorithm. The proposed methodology is tested in 18 real images from the MRI brain segmentation repository. The GM and WM distributions are accurately estimated. The α-stable distribution mixture model presented in this paper can be used as previous step in more complex MRI segmentation procedures using spatial information. Furthermore, due to the fact that the α-stable distribution is a generalization of the Gaussian distribution, the proposed methodology can be applied instead of the Gaussian mixture model, which is widely used in segmentation of brain MRI in the literature.
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16
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Liu S, Cai W, Wen L, Eberl S, Fulham MJ, Feng DD. Generalized regional disorder-sensitive-weighting scheme for 3D neuroimaging retrieval. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:7009-12. [PMID: 22255952 DOI: 10.1109/iembs.2011.6091772] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
3D functional neuroimaging is used in the diagnosis and management of neurological disorders. The efficient management and analysis of these large imaging datasets has prompted research in the field of content-based image retrieval. In this context, our generalized regional disorder-sensitive-weighting (DSW) scheme gives greater weight to brain regions affected by the diseases than regions that are relatively spared. We used two DSW matrices; one matrix is based on the occurrence maps that highlight abnormal functional regions; the other is based on the regional Fisher discriminant ratio. Our results suggest that our DSW matrices enhance neuroimaging data retrieval and provide a flexible weighting solution for the clinical analysis of different types of neurological disorders.
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Affiliation(s)
- Sidong Liu
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Australia
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17
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Besga A, Termenon M, Graña M, Echeveste J, Pérez JM, Gonzalez-Pinto A. Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett 2012; 520:71-6. [PMID: 22617636 DOI: 10.1016/j.neulet.2012.05.033] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Revised: 02/13/2012] [Accepted: 05/10/2012] [Indexed: 10/28/2022]
Abstract
The aim of this study is to look for differential effects in white matter (WM) of bipolar disorder (BD) and Alzheimer's disease (AD) patients. We proceed by investigating the feasibility of discriminating between BD and AD patients, and from healthy controls (HC), using multivariate data analysis based on diffusion tensor imaging (DTI) data features. Specifically, support vector machine (SVM) classifiers were trained and tested on fractional anisotropy (FA). Voxel sites are selected as features for classification if their Pearson's correlation between FA values at voxel site across subjects and the indicative variable specifying the subject class is above the threshold set by a percentile of its empirical distribution. To avoid double dipping, selection was performed only on training data in a leave one out cross-validation study. Classification results show that FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in AD vs. HC, BD vs. HC, and AD vs. BD leave-one-out cross-validation studies. The localization of the discriminant voxel sites on a probabilistic tractography atlas shows effects on seven major WM tracts in each hemisphere and two commissural tracts.
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Affiliation(s)
- A Besga
- Unidad de Investigación en Psiquiatría, Hospital Santiago Apostol, Vitoria-Gasteiz, Spain
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18
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Segovia F, Górriz J, Ramírez J, Salas-Gonzalez D, Álvarez I, López M, Chaves R. A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.03.050] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation. Neurosci Lett 2011; 502:225-9. [DOI: 10.1016/j.neulet.2011.07.049] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2011] [Revised: 07/16/2011] [Accepted: 07/26/2011] [Indexed: 11/18/2022]
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