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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification.

Imene Garali^1,2, Mouloud Adel¹, Salah Bourennane³

¹Aix Marseille Univ, CNRS, Centrale Marseille, Institut FresnelF-13013MarseilleFrance.

IEEE Journal of Translational Engineering in Health and Medicine

|April 12, 2018

Summary

This summary is machine-generated.

This study introduces a new method for analyzing Positron Emission Tomography (PET) scans to diagnose Alzheimer's disease (AD). Using the area under the curve (AUC) to rank brain regions effectively improves diagnostic classification accuracy.

Keywords:

Alzheimer’s disease Machine learning classification computer-aided diagnosis feature selection first order statistics positron emission tomography

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Area of Science:

Medical Imaging
Neuroscience
Artificial Intelligence

Background:

Positron emission tomography (PET) is crucial for diagnosing neurodegenerative diseases like Alzheimer's disease (AD).
Computer-aided diagnosis using medical image analysis aids in the quantitative evaluation of brain diseases.
Accurate identification of affected brain regions is vital for early AD diagnosis.

Purpose of the Study:

To present a novel method for ranking the effectiveness of brain volumes of interest (VOIs) in differentiating Alzheimer's disease (AD) patients from healthy controls using PET images.
To develop a quantitative approach for selecting the most informative brain regions for AD diagnosis.
To enhance the diagnostic performance of computer-aided diagnosis systems for neurodegenerative diseases.

Main Methods:

Brain PET images were mapped to anatomical volumes of interest (VOIs) using an atlas.
Histogram-based features were extracted from VOIs.
The area under the curve (AUC) was employed to select and rank VOIs based on their ability to discriminate between healthy and AD subjects.
Top-ranked VOIs were utilized as input for a support vector machine classifier.

Main Results:

The developed method successfully ranked brain VOIs based on their diagnostic effectiveness for Alzheimer's disease.
The area under the curve (AUC) parameter proved effective in creating a hierarchy of VOI discriminatory power.
Utilizing AUC for VOI selection outperformed traditional feature selection methods in classification accuracy for two-group separation.

Conclusions:

The proposed AUC-based VOI ranking method enhances the classification accuracy for distinguishing Alzheimer's disease from healthy controls using PET imaging.
This approach offers a more effective strategy for identifying critical brain regions in AD diagnosis.
The findings support the integration of advanced image analysis techniques for improved neurodegenerative disease diagnosis.