eprintid: 2775
rev_number: 9
eprint_status: archive
userid: 69
dir: disk0/00/00/27/75
datestamp: 2015-10-19 09:31:34
lastmod: 2015-10-19 09:31:34
status_changed: 2015-10-19 09:31:34
type: article
metadata_visibility: show
creators_name: Morisi, Rita
creators_name: Donini, Bruno
creators_name: Lanconelli, Nico
creators_name: Rosengarden, James
creators_name: Morgan, John
creators_name: Harden, Stephen
creators_name: Curzen, Nick
creators_id: rita.morisi@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
creators_id: 
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title: Semi-automated scar detection in delayed enhanced cardiac magnetic resonance images
ispublished: pub
subjects: QA75
subjects: RZ
subjects: T1
divisions: CSA
full_text_status: none
keywords: Image processing; Computer aided detection; Support vector machine
abstract: Late enhancement cardiac magnetic resonance images (MRI) has the ability to precisely delineate myocardial scars. We present a semi-automated method for detecting scars in cardiac MRI. This model has the potential to improve routine clinical practice since quantification is not currently offered due to time constraints. A first segmentation step was developed for extracting the target regions for potential scar and determining pre-candidate objects. Pattern recognition methods are then applied to the segmented images in order to detect the position of the myocardial scar. The database of late gadolinium enhancement (LE) cardiac MR images consists of 111 blocks of images acquired from 63 patients at the University Hospital Southampton NHS Foundation Trust (UK). At least one scar was present for each patient, and all the scars were manually annotated by an expert. A group of images (around one third of the entire set) was used for training the system which was subsequently tested on all the remaining images. Four different classifiers were trained (Support Vector Machine (SVM), k-nearest neighbor (KNN), Bayesian and feed-forward neural network) and their performance was evaluated by using Free response Receiver Operating Characteristic (FROC) analysis. Feature selection was implemented for analyzing the importance of the various features. The segmentation method proposed allowed the region affected by the scar to be extracted correctly in 96% of the blocks of images. The SVM was shown to be the best classifier for our task, and our system reached an overall sensitivity of 80% with less than 7 false positives per patient. The method we present provides an effective tool for detection of scars on cardiac MRI. This may be of value in clinical practice by permitting routine reporting of scar quantification.
date: 2015
date_type: published
publication: International Journal of Modern Physics C
volume: 26
number: 01
publisher: World Scientific
pagerange: 1550011
id_number: 10.1142/S0129183115500114
refereed: TRUE
issn: 0129-1831
official_url: http://www.worldscientific.com/doi/abs/10.1142/S0129183115500114
citation:   Morisi, Rita and Donini, Bruno and Lanconelli, Nico and Rosengarden, James and Morgan, John and Harden, Stephen and Curzen, Nick  Semi-automated scar detection in delayed enhanced cardiac magnetic resonance images.  International Journal of Modern Physics C, 26 (01).  p. 1550011.  ISSN 0129-1831      (2015)