Article

Brain morphometry by probabilistic latent semantic analysis

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P. BrambillaAuthors Info & Claims

MICCAI'10: Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II

Pages 177 - 184

Published: 20 September 2010 Publication History

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Abstract

The paper proposes a new shape morphometry approach that combines advanced classification techniques with geometric features to identify morphological abnormalities on the brain surface. Our aim is to improve the classification accuracy in distinguishing between normal subjects and schizophrenic patients. The approach is inspired by natural language processing. Local brain surface geometric patterns are quantized to visual words, and their co-occurrences are encoded as visual topic. To do this, a generative model, the probabilistic Latent Semantic Analysis is learned from quantized shape descriptors (visual words). Finally, we extract from the learned models a generative score, that is used as input of a Support Vector Machine (SVM), defining an hybrid generative/discriminative classification algorithm. An exhaustive experimental section is proposed on a dataset consisting of MRI scans from 64 patients and 60 control subjects. Promising results are reporting by observing accuracies up to 86.13%.

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Zhang FSong YCai WHauptmann ALiu SPujol SKikinis RFulham MFeng DChen M(2016)Dictionary pruning with visual word significance for medical image retrievalNeurocomputing10.1016/j.neucom.2015.11.008177:C(75-88)Online publication date: 12-Feb-2016
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Perina ABicego MCastellani UMurino V(2013)Exploiting geometry in counting gridsProceedings of the Second international conference on Similarity-Based Pattern Recognition10.1007/978-3-642-39140-8_17(250-264)Online publication date: 3-Jul-2013
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Published In

MICCAI'10: Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II

September 2010

691 pages

ISBN:3642157440

Editors:
Tianzi Jiang
The Chinese Academy of Sciences, Institute of Automation, Beijing, P. R. China
,
Nassir Navab
Technische Universität München, Institut für Informatik I16, Garching, Germany
,
Josien P. W. Pluim
University Medical Center Utrecht, Utrecht, The Netherlands
,
Max A. Viergever
University Medical Center Utrecht, Utrecht, The Netherlands

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Springer-Verlag

Berlin, Heidelberg

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Published: 20 September 2010

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Zhang FSong YCai WHauptmann ALiu SPujol SKikinis RFulham MFeng DChen M(2016)Dictionary pruning with visual word significance for medical image retrievalNeurocomputing10.1016/j.neucom.2015.11.008177:C(75-88)Online publication date: 12-Feb-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.11.008
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Perina ALovato PCristani MBicego M(2011)A comparison on score spaces for expression microarray data classificationProceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics10.5555/2075619.2075643(202-213)Online publication date: 2-Nov-2011
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Perina ALovato PMurino VBicego M(2010)Biologically-aware latent dirichlet allocation (BaLDA) for the classification of expression microarrayProceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics10.5555/1887854.1887877(230-241)Online publication date: 22-Sep-2010
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