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Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer's Disease Classification [chapter]

Yanxi Liu, Leonid Teverovskiy, Owen Carmichael, Ron Kikinis, Martha Shenton, Cameron S. Carter, V. Andrew Stenger, Simon Davis, Howard Aizenstein, James T. Becker, Oscar L. Lopez, Carolyn C. Meltzer
2004 Lecture Notes in Computer Science  
Discriminative image feature subspaces are computed, evaluated and selected automatically.  ...  We construct a computational framework for automatic central nervous system (CNS) disease discrimination using high resolution Magnetic Resonance Images (MRI) of human brains.  ...  Fig. 5 . 5 Examples of two automatically selected 3-feature discriminative subspaces for schizophrenia MR image data sets Fig. 6 . 6 (1): Sample LOO result showing the predicting power of the learned  ... 
doi:10.1007/978-3-540-30135-6_48 fatcat:b4qq4lwmencyldgdbuhbv4owuu

Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification

Y Liu
2018
This result indicates that the image intensity features and shape features complement each other in discriminative power for disease classification.  ...  For a given disease versus control data set and a local ROI, the algorithm is able to automatically find both the type and the location of the most discriminative image feature subsets for data visualization  ... 
doi:10.1184/r1/6554579 fatcat:3ng7hjmcqfg6ffefouwa3xvt5e

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Mohammad R. Arbabshirani, Sergey Plis, Jing Sui, Vince D. Calhoun
2017 NeuroImage  
of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease.  ...  Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed.  ...  Sui); Chinese National Science Foundation No. 81471367 and the State High-Tech Development Plan (863) No. 2015AA020513; Also, we would like to thank Monica Jaramillo for the initial survey of neuroimaging  ... 
doi:10.1016/j.neuroimage.2016.02.079 pmid:27012503 pmcid:PMC5031516 fatcat:7kxm7yeugrgvdlxmitccneqxc4

Automated identification of dementia using medical imaging: a survey from a pattern classification perspective

Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen
2015 Brain Informatics  
, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks.  ...  Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex-and ROI-based ones  ...  Thies W et al (2013) 2013 Alzheimer's disease facts and figures Alzheimer's association.  ... 
doi:10.1007/s40708-015-0027-x pmid:27747596 pmcid:PMC4883162 fatcat:yefc226j4za75afkkkolg4m5n4

Independent Component Analysis-Based Classification of Alzheimer's Disease MRI Data

Wenlu Yang, Ronald L.M. Lui, Jia-Hong Gao, Tony F. Chan, Shing-Tung Yau, Reisa A. Sperling, Xudong Huang
2011 Journal of Alzheimer's Disease  
We have thus proposed a method based on independent component analysis (ICA) for studying potential AD-related MR image features that can be coupled with the use of support vector machine (SVM) for classifying  ...  The MRI data were selected from the Open Access Series of Imaging Studies (OASIS) and the Alzheimer's Disease Neuroimaging Initiative databases.  ...  Municipality ADNI data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904).  ... 
doi:10.3233/jad-2011-101371 pmid:21321398 pmcid:PMC3697832 fatcat:dqrfpk75evelrdrtmt7dzoqtci

Efficient Morphometric Techniques in Alzheimer's Disease Detection: Survey and Tools

Vinutha N., P. Deepa Shenoy, P. Deepa Shenoy, K.R. Venugopal
2016 Neuroscience International  
The development of advance techniques in the multiple fields such as image processing, data mining and machine learning are required for the early detection of Alzheimer's Disease (AD) and to prevent the  ...  But in recent years, the main focus of researchers is towards the FBM and SBM to overcome the disadvantage of group analysis that existed in VBM and DBM.  ...  The corresponding author confirms that all of the other authors have read and approved the manuscript and there are no ethical issues involved.  ... 
doi:10.3844/amjnsp.2016.19.44 fatcat:3zeb2s5pjzfv7mptqi7cy2a3au

Translating state-of-the-art brain magnetic resonance imaging (MRI) techniques into clinical practice: multimodal MRI differentiates dementia subtypes in a traditional clinical setting

Taylor Kuhn, Sergio Becerra, John Duncan, Norman Spivak, Bianca Huan Dang, Barshen Habelhah, Kennedy D. Mahdavi, Michael Mamoun, Michael Whitney, F. Scott Pereles, Alexander Bystritsky, Sheldon E. Jordan
2021 Quantitative Imaging in Medicine and Surgery  
Twenty patients with dementia of the Alzheimer's type (DAT) and 18 patients with Parkinson's disease dementia (PDD) were identified using gold-standard techniques.  ...  All participants underwent multimodal MRI including T1 structural, diffusion tensor imaging (DTI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS).  ...  ASL and MRS poorly differentiated neurodegenerative groups Table 6 6 Discriminant function determining disease classification accuracy of multimodal MRI analyses differentiating Alzheimer's disease  ... 
doi:10.21037/qims-20-1355 pmid:34476189 pmcid:PMC8339641 fatcat:bhf3bsk4vnhvjjtiudzrrok4xe

Classification of schizophrenia using feature-based morphometry

U. Castellani, E. Rossato, V. Murino, M. Bellani, G. Rambaldelli, C. Perlini, L. Tomelleri, M. Tansella, P. Brambilla
2011 Journal of neural transmission  
Three steps were conducted: (1) landmark detection and description of the DLPFC, (2) feature vocabulary construction and Bag-of-Words (BoW) computation for brain representation, (3) SVM classification  ...  This integrated innovative ROI-SVM approach allows to reliably detect subjects with schizophrenia, based on a structural brain marker for the disease such as the DLPFC.  ...  Brambilla from the American Psychiatric Institute for Research and Education (APIRE), the Italian Ministry for University and Research, and the Italian Ministry of Health (IRCCS ''E. Medea'').  ... 
doi:10.1007/s00702-011-0693-7 pmid:21904897 fatcat:faefnwzoxjcbfmm2d3d2c77mhy

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images

Xiaobing Lu, Yongzhe Yang, Fengchun Wu, Minjian Gao, Yong Xu, Yue Zhang, Yongcheng Yao, Xin Du, Chengwei Li, Lei Wu, Xiaomei Zhong, Yanling Zhou (+9 others)
2016 Medicine  
Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI)  ...  These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological  ...  Distinct neuroanatomical profiles associated with SZ patients can provide a potential biomarker for disease diagnosis. Figure 6 . ROC curves of automatic classifications.  ... 
doi:10.1097/md.0000000000003973 pmid:27472673 pmcid:PMC5265810 fatcat:unmv2g676bbk7dhmex27hwd5ky

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

Li Zhang, Mingliang Wang, Mingxia Liu, Daoqiang Zhang
2020 Frontiers in Neuroscience  
We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where  ...  Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant  ...  AUTHOR CONTRIBUTIONS DZ, ML, and LZ designed this review. LZ and MW searched the literatures. LZ wrote this manuscript. All authors read, edited, and discussed the article.  ... 
doi:10.3389/fnins.2020.00779 pmid:33117114 pmcid:PMC7578242 fatcat:tzdcq3kyyrefvn7vxgdj5lnhju

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia

Manan Binth Taj Noor, Nusrat Zerin Zenia, M Shamim Kaiser, Shamim Al Mamun, Mufti Mahmud
2020 Brain Informatics  
This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer's disease, Parkinson's disease and schizophrenia—from  ...  The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting  ...  Acknowledgements The authors would like to thank the members of the acslab (http://www.acsla b.info/) for valuable discussions.  ... 
doi:10.1186/s40708-020-00112-2 pmid:33034769 pmcid:PMC7547060 fatcat:vvnq3knq4rgfrcwov3yr2s3mk4

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer's Disease

Pedro Paulo de Magalhães Oliveira, Ricardo Nitrini, Geraldo Busatto, Carlos Buchpiguel, João Ricardo Sato, Edson Amaro
2010 Journal of Alzheimer's Disease  
Here, we examine morphological changes in cortical thickness of patients with Alzheimer's disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification  ...  Data was analyzed using an automated algorithm for tissue segmentation and classification.  ...  Stevens, and N. Schmansky for helpful comments and support on the FreeSurfer functioning.  ... 
doi:10.3233/jad-2010-1322 pmid:20061613 fatcat:qfj767udezdnlf4adjv3yf2mua

Biomarker Evaluation by Multiple Kernel Learning for Schizophrenia Detection

Aydin Ulas, Umberto Castellani, Vittorio Murino, Marcella Bellani, Michele Tansella, Paolo Brambilla
2012 2012 Second International Workshop on Pattern Recognition in NeuroImaging  
We use eight different Regions of Interest (ROIs) extracted from Magnetic Resonance Images (MRIs). For each region we evaluate both tissue and geometric properties.  ...  In this paper, we use the promising paradigm of Multiple Kernel Learning (MKL) to challenge the problem of biomarker evaluation for schizophrenia detection.  ...  schizophrenia from MRI images and give the most accurate classification result.  ... 
doi:10.1109/prni.2012.12 dblp:conf/prni/UlasCMBTB12 fatcat:7dtwd7likvavhohnppvtw5dv7u

A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis [article]

Li Zhang and Mingliang Wang and Mingxia Liu and Daoqiang Zhang
2020 arXiv   pre-print
We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where  ...  Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant  ...  Deep Learning for Alzheimer's Disease Analysis Alzheimer's disease (AD) is a neurological, irreversible, progressive brain disorder and is the most common cause of dementia.  ... 
arXiv:2005.04573v1 fatcat:64ze55onzfemhgpebvsewe3fki

Multi-atlas based representations for Alzheimer's disease diagnosis

Rui Min, Guorong Wu, Jian Cheng, Qian Wang, Dinggang Shen
2014 Human Brain Mapping  
Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive  ...  achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification.  ...  INTRODUCTION Morphometric pattern analysis is one of the most popular approaches for automatic Alzheimer's disease (AD) diagnosis.  ... 
doi:10.1002/hbm.22531 pmid:24753060 pmcid:PMC4169318 fatcat:v7aytj2bszeejbsqrtevnnszzq
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