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Local Frequency Descriptor and Hybrid Features for Classification of Brain Magnetic Resonance Images using Ensemble Classifier

Shruthi G, Krishna Raj P M
2021 International Journal of Advanced Computer Science and Applications  
Further, the Local Frequency Descriptor (LFD) technique is employed to extract the prominent features from the brain tumor region.  ...  The hybrid features are extracted by analyzing the texture and statistical properties of brain MRI images.  ...  Local Frequency Descriptor is applied by authors in [14] on brain MRI images for studying the various properties of the brain tumor using Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns  ... 
doi:10.14569/ijacsa.2021.0121122 fatcat:mrhqzm3tyve5zoldgac32uv6tu

Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI

Kuldeep Kumar, Laurent Chauvin, Mathew Toews, Olivier Colliot, Christian Desrosiers
2017 arXiv   pre-print
However, due to the lack of a framework for comparing across multiple modalities, studies based on multi-modal data remain elusive.  ...  So far, fingerprinting studies have focused on identifying features from single-modality MRI data, which capture individual characteristics in terms of brain structure, function, or white matter microstructure  ...  Conclusion We presented a framework based on SIFT descriptors for the multi-modal analysis of genetically-related subjects.  ... 
arXiv:1709.06151v1 fatcat:jmfo3lhhinhxpowarxdr4ln6vy

Multi-Modal Analysis of Genetically-Related Subjects Using SIFT Descriptors in Brain MRI [chapter]

Kuldeep Kumar, Laurent Chauvin, Matthew Toews, Olivier Colliot, Christian Desrosiers
2018 Mathematics and Visualization  
Conclusion We presented a framework based on SIFT descriptors for the multi-modal analysis of genetically-related subjects.  ...  Building from the work presented in [12] , this framework represents images as a bags of features (BoF), where features are defined based on scale-invariant feature transform (SIFT) descriptors.  ... 
doi:10.1007/978-3-319-73839-0_17 fatcat:y25nvui66nal3ou3rgo7r3nmom

Three-dimensional texture analysis of MRI brain datasets

V.A. Kovalev, F. Kruggel, H.-J. Gertz, D.Y. von Cramon
2001 IEEE Transactions on Medical Imaging  
A method is proposed for three-dimensional (3-D) texture analysis of magnetic resonance imaging brain datasets.  ...  The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial classification tasks for pathologic findings in brain datasets.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their professionalism and fast review. They would also like to thank Dr. M. Svensen for proofreading the paper.  ... 
doi:10.1109/42.925295 pmid:11403201 fatcat:vwvh7wj5andqvcqzwsie76qr5q

Content-Based fMRI Brain Maps Retrieval [chapter]

Alba G. Seco de Herrera, L. Rodney Long, Sameer Antani
2016 Lecture Notes in Computer Science  
This work presents a CB fMRI retrieval approach based on the brain activation maps extracted using Probabilistic Independent Component Analysis (PICA).  ...  We obtained promising results on data from a variety of experiments which highlight the potential of the system as a tool that provides support for finding hidden similarities between brain activation  ...  Paul Kantor for providing us the fMRI experimental data.  ... 
doi:10.1007/978-3-319-47103-7_17 fatcat:dq7oijpe5fhvlfvzrermf6mbim

Computer Analysis Reveals Similarities between the Artistic Styles of Van Gogh and Pollock

Lior Shamir
2012 Leonardo: Journal of the International Society for the Arts, Sciences and Technology  
The analysis reveals that Vincent Van Gogh and Jackson Pollock share artistic styles that are far more similar to each other in terms of low-level image features compared to the similarity between the  ...  Here we use computer analysis to extract thousands of numerical low-level image content descriptors from digitized paintings, and use them to objectively compare the similarities between the artistic styles  ...  Acknowledgments This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging.  ... 
doi:10.1162/leon_a_00281 fatcat:vaefxjy45bcxbiolvec4l3nz7y

Shape-Attributes of Brain Structures as Biomarkers for Alzheimer's Disease

Tanya Glozman, Justin Solomon, Franco Pestilli, Leonidas Guibas, Yudong Zhang
2017 Journal of Alzheimer's Disease  
Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions.  ...  We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures.  ...  Adrian Butscher for helpful discussions, resources, and Michael Perry for technical support. T Authors' disclosures available online (http://j-alz. com/manuscript-disclosures/16-0900r1).  ... 
doi:10.3233/jad-160900 pmid:27911322 pmcid:PMC5240557 fatcat:btjg2ls4rjdktlmuqtt4rxz5qy

Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization

Sidong Liu, Weidong Cai, Lingfeng Wen, David Dagan Feng, Sonia Pujol, Ron Kikinis, Michael J. Fulham, Stefan Eberl
2014 Computerized Medical Imaging and Graphics  
[4] designed a neuroimaging retrieval system with four 3D feature descriptors based on 100 brain Magnetic Resonance Imaging (MRI) studies. Unay et al.  ...  Single analysis approaches were also compared. (a) Feature Optimization for AD. (b) Feature Optimization for MCI.  ... 
doi:10.1016/j.compmedimag.2014.05.003 pmid:24933011 pmcid:PMC4135007 fatcat:5wm6hdbatjc27as36pdqjclnki

Characterization and Classification of Brain Tissue and Stroke Lesions in Non-Contrast Computed Tomography Images of Stroke Patients Using Statistical Texture Descriptors and Artificial Neural Network

Christopher C. Ohagwu, Kenneth K. Agwu, Christian O. Onyekelu, Hameed Mohammad, Mohammed Abba
2022 Journal of Radiography and Radiation Sciences  
Aim: To characterize and classify stroke lesions and normal brain tissue in computed tomography (CT) images using statistical texture descriptors.  ...  Raw data analysis was performed to identify the parameters that best discriminate between normal brain tissue and stroke lesions.  ...  The image analysis carried out in this study was texture analysis in which statistical texture parameters were calculated for selected ROIs in the image.  ... 
doi:10.48153/jrrs.v32i1.223080 fatcat:4n5kpwnqsvemdooajjskgc4vc4

Blood vessel feature description for detection of Alzheimers disease

Musab Sahrim, Mark S. Nixon, Roxana O. Carare
2014 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV)  
We describe how image analysis can be used to detect the presence of Alzheimer's disease. The data are images of brain tissue collected from subjects with and without Alzheimer's disease.  ...  These measures form a feature vector which is derived from the images of brain tissue, and the discrimination capability shows that it is possible to detect the presence of Alzheimer's disease using these  ...  Note that so far the study is in vitro and this study is sufficiently encouraging for translation to in vivo 3D MRI image analysis.  ... 
doi:10.1109/icarcv.2014.7064325 dblp:conf/icarcv/SahrimNC14 fatcat:k3nwrr6m2nhvtntdjckah7vnwa

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  
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.  ...  With the increased need for multi-center magnetic resonance imaging studies, problems arise related to differences in hardware and software between centers.  ...  When choosing the model, we take a few important considerations into account: i) we have 16 image-extracted features plus age; ii) some of these are related only to one of the brain volumes; iii) the features  ... 
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.  ...  The features are quantized using the Bag-of-Visual-Words approach to build one signature by brain (subject).  ...  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

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

M. Zhou, J. Scott, B. Chaudhury, L. Hall, D. Goldgof, K.W. Yeom, M. Iv, Y. Ou, J. Kalpathy-Cramer, S. Napel, R. Gillies, O. Gevaert (+1 others)
2017 American Journal of Neuroradiology  
We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights.  ...  We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making.  ...  In general, tumor sizes based on these images are used for monitoring tumor response to therapy. 13 Thus, radiomic models for brain tumor analysis [14] [15] [16] often focus on contrast-enhanced sequences  ... 
doi:10.3174/ajnr.a5391 pmid:28982791 pmcid:PMC5812810 fatcat:fovsdrilurg4xp7ghpjimk7pge

A 3D difference-of-Gaussian-based lesion detector for brain PET

Weidong Cai, Sidong Liu, Yang Song, Sonia Pujol, Ron Kikinis, Dagan Feng
2014 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)  
To capture these important features, we propose a novel lesion detector with three lesion-centric feature descriptors for brain PET.  ...  The preliminary results show that the proposed lesion detector is robust in capturing the brain lesions and has a great potential to be a predictive biomarker for neurological disorders.  ...  CONCLUSION This paper presents a new lesion detector coupled with three lesion-centric feature descriptors for brain PET analysis.  ... 
doi:10.1109/isbi.2014.6867961 fatcat:tcpzqqqcwnfybmqgfypleulbcq

Neuroanatomic-Based Detection Algorithm for Automatic Labeling of Brain Structures in Brain Injury [chapter]

M. Luna, F. Gayá, A. García-Molina, L. M. González, C. Cáceres, M. Bernabeu, T. Roig, A. Pascual-Leone, J. M. Tormos, E. J. Gómez
2014 International Federation for Medical and Biological Engineering Proceedings  
This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical  ...  The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient.  ...  CONCLUSIONS One of the main challenges of neuroimaging is to develop robust automated image analysis methods to detect brain injury features.  ... 
doi:10.1007/978-3-319-00846-2_418 fatcat:lhyfr2lexnh5jpjzs2gpubd6v4
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