eprintid: 3025
rev_number: 8
eprint_status: archive
userid: 69
dir: disk0/00/00/30/25
datestamp: 2016-01-20 10:27:26
lastmod: 2016-04-06 10:06:49
status_changed: 2016-01-20 10:27:26
type: book_section
metadata_visibility: show
creators_name: Mukhopadhyay, Anirban
creators_name: Oksuz, Ilkay
creators_name: Tsaftaris, Sotirios A.
creators_id: 
creators_id: ilkay.oksuz@imtlucca.it
creators_id: sotirios.tsaftaris@imtlucca.it
title: Supervised Learning of Functional Maps for Infarct Classification
ispublished: pub
subjects: QA75
subjects: RZ
subjects: T1
divisions: CSA
full_text_status: none
keywords: Infarct, Cardiac remodeling, Laplace-Beltrami, SV, SVD
abstract: Our submission to the STACOM Challenge at MICCAI 2015 is based on the supervised learning of functional map representation between End Systole (ES) and End Diastole (ED) phases of Left Ventricle (LV), for classifying infarcted LV from the healthy ones. The Laplace-Beltrami eigen-spectrum of the LV surfaces at ES and ED, represented by their triangular meshes, are used to compute the functional maps. Multi-scale distortions induced by the mapping, are further calculated by singular value decomposition of the functional map. During training, the information of whether an LV surface is healthy or diseased is known, and this information is used to train an SVM classifier for the singular values at multiple scales corresponding to the distorted areas augmented with surface area difference of epicardium and endocardium meshes. At testing similar augmented features are calculated and fed to the SVM model for classification. Promising results are obtained on both cross validation of training data as well as on testing data, which encourages us in believing that this algorithm will perform favourably in comparison to state of the art methods.
date: 2016
date_type: published
series: Lecture Notes in Computer Science
volume: 9534
publisher: Springer
pagerange: 162-170
pages: 8
id_number: 10.1007/978-3-319-28712-6_18
refereed: TRUE
isbn: 978-3-319-28711-9
book_title: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
official_url: http://dx.doi.org/10.1007/978-3-319-28712-6_18
citation:   Mukhopadhyay, Anirban and Oksuz, Ilkay and Tsaftaris, Sotirios A.  Supervised Learning of Functional Maps for Infarct Classification.   In:  Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges.   Lecture Notes in Computer Science, 9534 .  Springer, pp. 162-170.  ISBN 978-3-319-28711-9      (2016)