Histogram-Based Features Selection and Volume Of Interest Ranking For Brain PET Image Classification

March 19, 2018

Imène GaraliMouloud AdelSalah BourennaneEric Guedj

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

Abstract

Histogram-Based Features Selection and Volume Of Interest Ranking For Brain PET Image Classification

Positron Emission Tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative diseases diagnosis. Computer-Aided Diagnosis (CAD), based on medical image analysis could help quantitative evaluation of brain diseases such as Alzheimer’s Disease (AD). A novel method of ranking the effectiveness of brain Volume Of Interest (VOI) to separate healthy control (HC) from AD brains PET images is presented in this paper. Brain images are first mapped into anatomical VOIs using an atlas. Histogram-based features are then extracted and used to select and rank VOIs according to the Area Under Curve (AUC) parameter, which produces a hierarchy of the ability of VOIs to separate between groups of subjects. The top-ranked VOIs are then input into a Support Vector Machine (SVM) classifier. The developed method is evaluated on a local data base image and compared to known selection feature methods. Results show that using AUC outperforms classification results in the case of a two group separation.

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