3-D MRI Brain Scan Feature Classification Using an Oct-Tree Representation



Udomchaiporn, Akadej, Coenen, Frans ORCID: 0000-0003-1026-6649, García-Fiñana, Marta and Sluming, Vanessa
(2013) 3-D MRI Brain Scan Feature Classification Using an Oct-Tree Representation. In: ADMA'13, Zhejiang University.

[img] Text
Akadej-MIUA14.pdf - Submitted version

Download (155kB)

Abstract

This paper presents a procedure for the classification of specific 3-D features in Magnetic Resonance Imaging (MRI) brain scan volumes. The main contributions of the paper are: (i) a proposed Bounding Box segmentation technique to extract the 3-D features of interest from MRI volumes, (ii) an oct-tree technique to represent the extracted sub-volumes and (iii) a frequent sub-graph mining based feature space mechanism to support classification. The proposed process was evaluated using 210 3-D MRI brain scans of which 105 were from "healthy" people and 105 from epilepsy patients. The features of interest were the left and right ventricles. Both the process and the evaluation are fully described. The results indicate that the proposed process can be effectively used to classify 3-D MRI brain scan features. ©Springer-Verlag 2013.

Item Type: Conference or Workshop Item (Unspecified)
Additional Information: ## TULIP Type: Conference Proceedings (contribution) ##
Depositing User: Symplectic Admin
Date Deposited: 07 Feb 2017 15:42
Last Modified: 19 Jan 2023 07:19
DOI: 10.1007/978-3-642-53914-5_20
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3005528