Authors:
Katarina Trojacanec
;
Ivan Kitanovski
;
Ivica Dimitrovski
and
Suzana Loshkovska
Affiliation:
Faculty of Computer Science and Engineering, “Ss. Cyril and Methodius” University and Skopje, Macedonia, The Former Yugoslav Republic of
Keyword(s):
CBIR, Alzheimer’s Disease, VOI, Segmentation, Feature Extraction, Feature Selection, MRI, ADNI.
Related
Ontology
Subjects/Areas/Topics:
Bioimaging
;
Biomedical Engineering
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Feature Recognition and Extraction Methods
;
Health Engineering and Technology Applications
;
Magnetic Resonance Imaging
;
Medical Imaging and Diagnosis
;
NeuroSensing and Diagnosis
;
Neurotechnology, Electronics and Informatics
Abstract:
The aim of the paper is to present Content Based Retrieval of MRI based on the brain structure changes
characteristic for Alzheimer’s Disease (AD). The approach used in this paper aims to improve the retrieval
performance while using smaller number of features in comparison to the descriptor dimensionality
generated by the traditional feature extraction techniques. The feature vector consists of the measurements
of cortical and subcortical brain structures, including volumes of the brain structures and cortical thickness.
Two main stages are required to obtain these features: segmentation and calculation of the quantitative
measurements. The feature subset selection is additionally applied using Correlation-based Feature
Selection (CFS) method. Euclidean distance is used as a similarity measurement. The retrieval performance
is evaluated using MRIs provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Experimental results show that the strategy used in this research out
performs the traditional one despite its
simplicity and small number of features used for representation.
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