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Updated: Jun 8, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
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Brain morphometry by probabilistic latent semantic analysis.

U Castellani1, A Perina, V Murino

  • 1VIPS lab, University of Verona, Italy.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 1, 2010
PubMed
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This summary is machine-generated.

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This study introduces a novel brain imaging analysis method using natural language processing techniques to detect schizophrenia-related brain shape differences. The hybrid approach achieved up to 86.13% accuracy in distinguishing patients from controls.

Area of Science:

  • Neuroimaging
  • Medical image analysis
  • Computational anatomy

Background:

  • Schizophrenia diagnosis relies on clinical symptoms, lacking objective biomarkers.
  • Brain morphometry studies show structural differences in schizophrenic patients.
  • Current shape analysis methods may not fully capture complex geometric variations.

Purpose of the Study:

  • To develop an advanced shape morphometry approach for improved classification of schizophrenia.
  • To leverage natural language processing concepts for brain surface geometric feature extraction.
  • To create a hybrid generative/discriminative model for enhanced diagnostic accuracy.

Main Methods:

  • Quantizing local brain surface geometric patterns into 'visual words'.
  • Employing Probabilistic Latent Semantic Analysis (pLSA) to model 'visual topics' from these descriptors.

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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
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Related Experiment Videos

Last Updated: Jun 8, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

  • Integrating generative scores from pLSA into a Support Vector Machine (SVM) classifier.

    • Main Results:

    • The hybrid generative/discriminative approach demonstrated high classification performance.
    • Achieved diagnostic accuracies up to 86.13% on a dataset of MRI scans.
    • Successfully identified morphological abnormalities indicative of schizophrenia.

    Conclusions:

    • The proposed shape morphometry technique offers a promising tool for objective schizophrenia detection.
    • The novel application of natural language processing principles enhances brain surface analysis.
    • This method has the potential to aid in early and accurate diagnosis of schizophrenia.