Filters








1,359 Hits in 5.3 sec

Volumetric Texture Description and Discriminant Feature Selection for MRI [chapter]

Abhir Bhalerao, Constantino Carlos Reyes-Aldasoro
2003 Lecture Notes in Computer Science  
doi:10.1007/978-3-540-45210-2_52 fatcat:qofp7xsbyvbvhlbe6v3u2rbyqy

Volumetric Texture Description and Discriminant Feature Selection for MRI [chapter]

Constantino Carlos Reyes-Aldasoro, Abhir Bhalerao
2003 Lecture Notes in Computer Science  
doi:10.1007/978-3-540-45087-0_24 fatcat:mwemf4bp2ze7vlcq37pvytc36m

Multidimensional Texture Characterization: On Analysis for Brain Tumor Tissues Using MRS and MRI

Deepa Subramaniam Nachimuthu, Arunadevi Baladhandapani
2014 Journal of digital imaging  
The volumetric features extracted from the vectors of this matrix articulate some important elementary structures, which along with spectroscopic metabolite ratios discriminate the tumor grades and tissue  ...  The quantitative 3D analysis reveals significant improvement in terms of global accuracy rate for automatic classification in brain tissues and discriminating pathological tumor tissue from structural  ...  3D (volumetric) textures using MRI, MRS, and both MRI and MRS [5, 9, 10, 24, 25] .  ... 
doi:10.1007/s10278-013-9669-5 pmid:24496552 pmcid:PMC4090400 fatcat:d5ayjha64rd6tejmv5fjoqph4m

Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification

Constantino Carlos Reyes Aldasoro, Abhir Bhalerao
2007 IEEE Transactions on Medical Imaging  
A novel Bhattacharyya space, based on the Bhattacharyya distance, is proposed for selecting the most discriminant measurements and producing a compact feature space.  ...  In this paper a Multiresolution Volumetric Texture Segmentation (M-VTS) algorithm is presented.  ...  Early work of Haralick [4] is a standard reference for statistical and structural approaches for texture description.  ... 
doi:10.1109/tmi.2006.884637 pmid:17243580 fatcat:lv45jloa3far3pozzpwi7x3jdi

Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

R. Rajesh Sharma, P. Marikkannu
2015 The Scientific World Journal  
The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.  ...  The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the  ...  They are (a) micro-macro feature extraction for each voxel of VOI as 3D volumetric data, (b) optimal feature subset selection using refined gravitational search algorithm from extracted features, (c) the  ... 
doi:10.1155/2015/184350 pmid:26509188 pmcid:PMC4609875 fatcat:bf47vuv2obcddoxrpeerxqsus4

Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment [article]

Imon Banerjee, Lewis Hahn, Geoffrey Sonn, Richard Fan, Daniel L. Rubin
2016 arXiv   pre-print
We found that a group of 44 discriminative predictors among 1464 quantitative imaging features can be used to produce an area under the ROC curve of 0.73.  ...  We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been  ...  This work was supported in part by grants from the National Cancer Institute, National Institutes of Health, U01CA142555, 1U01CA190214, and 1U01CA187947.  ... 
arXiv:1612.00408v1 fatcat:mfvynuqppjcprjnykl764dhxsi

Assessment of Linear Discrimination and Nonlinear Discrimination Analysis in Diagnosis Alzheimer's Disease in Early Stages

Roya Golestani, Akbar Gharbali, Surena Nazarbaghi
2020 Advances in Alzheimer s Disease  
Linear discriminant analysis and nonlinear discriminant analysis were used for texture analysis.  ...  The purpose of this study is to evaluate discriminating power of two texture analysis, linear discriminant analysis and nonlinear discriminant analysis, in classifying atrophy of Alzheimer's disease and  ...  We just used digital features of MRI image so that no special criteria of patients included for such study.  ... 
doi:10.4236/aad.2020.92002 fatcat:dsfacflhffc3xi6r3j3pszdqge

Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications [chapter]

Andrés Larroza, Vicente Bodí, David Moratal
2016 Assessment of Cellular and Organ Function and Dysfunction using Direct and Derived MRI Methodologies  
In the present chapter, we describe texture analysis as a process consisting of six steps: MRI acquisition, region of interest (ROI) definition, ROI preprocessing, feature extraction, feature selection  ...  Texture analysis is a technique used for the quantification of image texture.  ...  The feature selection and classification steps are not specific for texture analysis, so instead of providing a full description of the existing methods, we briefly describe the two classifiers mostly  ... 
doi:10.5772/64641 fatcat:sw7nodso7rfpfjohaopmrt2zta

Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis of Deformation Fields in Huntington's Disease [article]

Kilian Hett, Hans Johnson, Pierrick Coupé, Jane Paulsen, Jeffrey Long, Ipek Oguz
2020 arXiv   pre-print
We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls.  ...  a primary imaging-based marker for the study of Huntington's disease.  ...  This further allows us to use feature selection techniques to select the most discriminant voxels [5] .  ... 
arXiv:2001.08651v1 fatcat:x7ss4wtxrnh4tma57xav23g75y

Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis Of Deformation Fields in Huntington's Disease

Kilian Hett, Hans Johnson, Pierrick Coupe, Jane S. Paulsen, Jeffrey D. Long, Ipek Oguz
2020 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)  
We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls.  ...  a primary imaging-based marker for the study of Huntington's disease.  ...  This further allows us to use feature selection techniques to select the most discriminant voxels [5] .  ... 
doi:10.1109/isbi45749.2020.9098692 pmid:34873434 pmcid:PMC8643362 dblp:conf/isbi/HettJCPLO20 fatcat:vouhyz7p3ff5vhfdqsasbubfsa

Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer

Ming Fan, Hui Li, Shijian Wang, Bin Zheng, Juan Zhang, Lihua Li, Alessandro Weisz
2017 PLoS ONE  
We identified clinical information and 3D imaging features from DCE-MRI as candidate biomarkers for discriminating among four molecular subtypes of breast cancer. (2017) Radiomic analysis reveals DCE-MRI  ...  An evolutionary algorithm was used to select an optimal subset of features for classification.  ...  ) (2013CB329502), and the Natural Science Foundation of Zhejiang Province of China (LZ15F010001 and LQ14F010011).  ... 
doi:10.1371/journal.pone.0171683 pmid:28166261 pmcid:PMC5293281 fatcat:atrgj76ymnhd7ncs2y6kstcpuy

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry

Lauge Sørensen, Christian Igel, Akshay Pai, Ioana Balas, Cecilie Anker, Martin Lillholm, Mads Nielsen
2017 NeuroImage: Clinical  
Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry Sørensen,  ...  Acknowledgments This work was supported in part by the Danish National Advanced Technology Foundation (project 034-2011-5, "Early MRI diagnosis of Alzheimer's Disease") and in part by Eurostars (project  ...  ADNI acknowledgments: ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following:  ... 
doi:10.1016/j.nicl.2016.11.025 pmid:28119818 pmcid:PMC5237821 fatcat:3c2rqkbpfffa5ebnlo3wfbdbta

Review on Brain Tumor Segmentation and Classification Techniques

N S Zulpe, V P Pawar
2017 International Journal of Engineering Research and  
, and ANN image segmentation techniques.  ...  Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy.  ...  They explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI.  ... 
doi:10.17577/ijertv6is110008 fatcat:lh6yklz5cfen7nwsgjwblalz4q

Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases

Vincenza Granata, Roberta Fusco, Antonio Avallone, Alfonso De Stefano, Alessandro Ottaiano, Carolina Sbordone, Luca Brunese, Francesco Izzo, Antonella Petrillo
2021 Cancers  
Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation.  ...  Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered.  ...  Moreover, the authors are grateful to Antonio Daniele and Assunta Zazzaro for the collaboration. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers13030453 pmid:33504085 fatcat:fzcwk44ys5bbhgocoyb2vopesa

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

Ahmad Chaddad, Christian Desrosiers, Lama Hassan, Camel Tanougast
2017 BMC Neuroscience  
Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences  ...  An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects.  ...  Acknowledgements The authors would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) and the École de Technologie Supérieure for supporting this work.  ... 
doi:10.1186/s12868-017-0373-0 pmid:28821235 fatcat:gbktdw4jofbzbe53gxiyvy7es4
« Previous Showing results 1 — 15 out of 1,359 results