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Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors [chapter]

Maria Ines Meyer, Ezequiel de la Rosa, Koen Van Leemput, Diana M. Sima
2019 Lecture Notes in Computer Science  
Then, we train a Relevance Vector Machine (RVM) model over a large multi-site dataset of healthy subjects to perform volume harmonization.  ...  In this work, we explore a novel approach to harmonize brain volume measurements by using only image descriptors. First, we explore the relationships between volumes and image descriptors.  ...  The Relevance Vector Machine for data harmonization To harmonize brain volumes, we subtract correction terms based on estimated variability trends from the original volumes.  ... 
doi:10.1007/978-3-030-32695-1_9 fatcat:rfcdxbcwobggfdaoww432smqqy

Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex

Olfa Ben Ahmed, Maxim Mizotin, Jenny Benois-Pineau, Michèle Allard, Gwénaëlle Catheline, Chokri Ben Amar
2015 Computerized Medical Imaging and Graphics  
Classical approaches in visual information retrieval have been successfully used for analysis of structural MRI brain images.  ...  This yields a transformation of a full 3D image of brain ROIs into a 1D signature, a histogram of quantized features. To reduce the dimensionality of the signature, we use the PCA technique.  ...  Acknowledgments Data collection and sharing for this project was funded by the Alzheimer's Disease  ... 
doi:10.1016/j.compmedimag.2015.04.007 pmid:26069906 fatcat:nq7sdazhivfvnmyzzykaqq4dii

Classification of Alzheimer's disease subjects from MRI using hippocampal visual features

Olfa Ben Ahmed, Jenny Benois-Pineau, Michèle Allard, Chokri Ben Amar, Gwénaëlle Catheline
2014 Multimedia tools and applications  
In this paper, we develop an automatic classification framework for AD recognition in structural Magnetic Resonance Images (MRI).  ...  Indexing and classification tools for Content Based Visual Information Retrieval (CBVIR) have been penetrating the universe of medical image analysis.  ...  In [15] [16] [13] , shape information in the form of spherical harmonics (SH) has been used as features in the support vector machine (SVM) classifier.  ... 
doi:10.1007/s11042-014-2123-y fatcat:sxc25jbffbbfvifxwlva3tw2r4

Multimodal classification of Dementia using functional data, anatomical features and 3D invariant shape descriptors

Arthur Mikhno, Pablo Martinez Nuevo, Davangere P. Devanand, Ramin V. Parsey, Andrew F. Laine
2012 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)  
MRI gray matter volume and FDG-PET average intensity from 93 regions were used as features in a Support Vector Machine (SVM) classifier [4] .  ...  We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region.  ...  In order to achieve this property and use them as invariant descriptors we can construct the following vector: of (2l + 1) dimensionality.  ... 
doi:10.1109/isbi.2012.6235621 pmid:24576927 pmcid:PMC3934109 dblp:conf/isbi/MikhnoNDPL12 fatcat:uaocf4tjxjf7jcxyffenolilqu

Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region

Prateek Keserwani, V. S. Chandrasekhar Pammi, Om Prakash, Ashish Khare, Moongu Jeon
2016 International Journal of Image Graphics and Signal Processing  
Then, hippocampus region was extracted from brain structural MRI images, followed by application of two dimensional Gabor filter in three scales and eight orientations for texture computation.  ...  Hence, this methodology could be used as diagnostic measure for the detection of Alzheimer disease.  ...  In [12] authors used CHF visual feature descriptor and volume of CSF pixel of hippocampal region.  ... 
doi:10.5815/ijigsp.2016.06.02 fatcat:i4abkbjcarcb7evlmzjoyrrtgy

Local Kernel for Brains Classification in Schizophrenia [chapter]

U. Castellani, E. Rossato, V. Murino, M. Bellani, G. Rambaldelli, M. Tansella, P. Brambilla
2009 Lecture Notes in Computer Science  
In this paper a novel framework for brain classification is proposed in the context of mental health research.  ...  A learning by example method is introduced by combining local measurements with non linear Support Vector Machine.  ...  The dataset used in this work is part of a larger database cared by the Research Unit on Brain Imaging and Neuropsychology (RUBIN) at the Department of Medicine and Public Health-Section of Psychiatry  ... 
doi:10.1007/978-3-642-10291-2_12 fatcat:z7tgyakllrc4vda6rsc6wfythu

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  
The objective of this study was to use a combined local descriptor, namely scale invariance feature transform (SIFT), and a non linear support vector machine (SVM) technique to automatically classify patients  ...  Keywords Neuroimaging Á MRI Á Support vector machine Á Dorsolateral prefrontal cortex Á Shape morphometry The preliminary results of this study were presented at the XI  ...  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

Unraveling the MRI-Based Microstructural Signatures Behind Primary Progressive and Relapsing-Remitting Multiple Sclerosis Phenotypes

Ilaria Boscolo Galazzo, Lorenza Brusini, Muge Akinci, Federica Cruciani, Marco Pitteri, Stefano Ziccardi, Albulena Bajrami, Marco Castellaro, Ahmed M A Salih, Francesca B Pizzini, Jorge Jovicich, Massimiliano Calabrese (+1 others)
2021 Journal of Magnetic Resonance Imaging  
For dMRI, both diffusion tensor imaging and 3D simple harmonics oscillator based reconstruction and estimation models were used for feature extraction from a predefined set of regions.  ...  A support vector machine (SVM) was used to perform patients' classification relying on all these measures.  ...  Acknowledgments We thank Alberto De Luca and Agnese Tamanti for the helpful discussions on dMRI pre-processing, and all the technicians and clinicians that helped with MRI and clinical data collection.  ... 
doi:10.1002/jmri.27806 pmid:34189804 pmcid:PMC9290631 fatcat:44puaafxsrbu7ev67gsjx3eshy

RELIEFF FEATURE SELECTION BASED ALZHEIMER DISEASE CLASSIFICATION USING HYBRID FEATURES AND SUPPORT VECTOR MACHINE IN MAGNETIC RESONANCE IMAGING

HALEBEDU SUBBARAYA SURESHA, S.S. PARTHASARATHI
2019 INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY  
After performing histogram equalization and skull removal of the collected brain images, segmentation was carried-out using Fuzzy C-Means (FCM) for segmenting the white matter, Cerebro-Spinal Fluid (CSF  ...  segmented brain images.  ...  (PCA) + SVM [16] , Circular Harmonic Functions (CHFs) descriptors + Posterior Cingulate Cortex (PCC) [17] , and fusion of volume [18] ) on a reputed dataset ADNI.  ... 
doi:10.34218/ijcet.10.1.2019.015 fatcat:35wzoi66mng6ne4djws3qqvtbe

Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction

Angela Lombardi, Nicola Amoroso, Domenico Diacono, Alfonso Monaco, Sabina Tangaro, Roberto Bellotti
2020 Brain Sciences  
In this work we evaluated three different harmonization techniques on the Autism Brain Imaging Data Exchange (ABIDE) dataset for age prediction analysis in two groups of subjects (i.e., controls and autism  ...  A machine learning framework was developed to quantify the effects of the different harmonization strategies on the final performance of the models and on the set of morphological features that are relevant  ...  for the 34 cortical brain regions of each hemisphere; and, • global brain metrics, including surface and volume statistics of each hemisphere; total cerebellar gray and white matter volume, brainstem  ... 
doi:10.3390/brainsci10060364 pmid:32545374 fatcat:7uf3nynaevfxjbey4ee4r6gdky

Predict Alzheimer's disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations

Sreevani Katabathula, Qinyong Wang, Rong Xu
2021 Alzheimer's Research & Therapy  
Background Alzheimer's disease (AD) is a progressive and irreversible brain disorder. Hippocampus is one of the involved regions and its atrophy is a widely used biomarker for AD diagnosis.  ...  DenseCNN2 was compared with other state-of-the-art machine learning approaches for the task of AD classification.  ...  Acknowledgements We thank the Alzheimer's Disease Neuroimaging Initiative (ADNI) for generously sharing clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's  ... 
doi:10.1186/s13195-021-00837-0 pmid:34030743 fatcat:ujjf6osodzcnbnq3btnsp7z6am

A Survey on Classification algorithms of Brain Images in Alzheimer's disease based on Feature Extraction techniques

Ruhul Amin Hazarika, Arnab Kumar Maji, Samarendra Nath Sur, Babu Sena Paul, Debdatta Kandar
2021 IEEE Access  
The authors have used circular harmonic function to select the contrasting patterns, and their coefficients form the descriptors of brain pattern.  ...  PCA based approach is used the extract the relevant feature vectors using the polynomial kernels and RBF for non-linear vector forms.  ... 
doi:10.1109/access.2021.3072559 fatcat:cc4ffd325naozaxs63geaut76i

Performance Analysis of Texture Image Retrieval for Curvelet, Contourlet Transform and Local Ternary Pattern Using Mri Brain Tumor Image

Anbarasa Pandian A, Balasubramanian R
2015 International Journal in Foundations of Computer Science & Technology  
Brain tumor is an abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to detect the MRI brain tumor images.  ...  The Experiment is performed on a collection of 1000 brain tumor images with different orientations.  ...  The Classification is based on advanced kernel based techniques such as Support Vector Machine (SVM) and the Relevance Vector Machine (RVM) is used for normal and abnormal are deployed.  ... 
doi:10.5121/ijfcst.2015.5604 fatcat:t7yykmygfrfptarvvzeyvap3oa

Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding

Jie Zhang, Yonghui Fan, Qingyang Li, Paul M. Thompson, Jieping Ye, Yalin Wang
2017 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)  
Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during  ...  Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD).  ...  For the comparison purpose, the raw cortical thickness data from FreeSurfer, whole brain volume and area calculated by FreeSurfer were also used as features with SVM as the classifier on the same set of  ... 
doi:10.1109/isbi.2017.7950557 pmid:28959379 pmcid:PMC5613953 dblp:conf/isbi/ZhangFLTYW17 fatcat:niyechvjq5axjfje4augcjc7te

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals

Martin Rozycki, Theodore D Satterthwaite, Nikolaos Koutsouleris, Guray Erus, Jimit Doshi, Daniel H Wolf, Yong Fan, Raquel E Gur, Ruben C Gur, Eva M Meisenzahl, Chuanjun Zhuo, Hong Yin (+4 others)
2017 Schizophrenia Bulletin  
Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia  ...  Taken together, these results emphasize the potential for structural neuroimaging data to provide a robust and reproducible imaging signature of schizophrenia.  ...  Acknowledgments At the University of Pennsylvania site, thanks to Monica Calkins, PhD for assistance with clinical phenotyping.  ... 
doi:10.1093/schbul/sbx137 pmid:29186619 fatcat:wazhaxiajfh6zlq5zrrrcc2wdm
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