Semi-automated scar detection in delayed enhanced cardiac magnetic resonance images

Rita Morisi, Bruno Donini, Nico Lanconelli (), James Rosengarden, John Morgan, Stephen Harden and Nick Curzen
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Rita Morisi: IMT Institute for Advanced Studies, Piazza S. Ponziano, 6, 55100, Lucca, Italy;
Bruno Donini: Dipartimento di Fisica e Astronomia, Alma Mater Studiorum, University of Bologna, Viale Berti-Pichat 6/2, 40127 Bologna, Italy
Nico Lanconelli: Dipartimento di Fisica e Astronomia, Alma Mater Studiorum, University of Bologna, Viale Berti-Pichat 6/2, 40127 Bologna, Italy
James Rosengarden: University Hospital Southampton NHS Foundation Trust, Tremona Rd, Southampton SO16 6YD, UK
John Morgan: University Hospital Southampton NHS Foundation Trust, Tremona Rd, Southampton SO16 6YD, UK
Stephen Harden: University Hospital Southampton NHS Foundation Trust, Tremona Rd, Southampton SO16 6YD, UK
Nick Curzen: University Hospital Southampton NHS Foundation Trust, Tremona Rd, Southampton SO16 6YD, UK;

International Journal of Modern Physics C (IJMPC), 2015, vol. 26, issue 01, 1-17

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.

Keywords: Image processing; computer aided detection; support vector machine; 87.61.Tg; 87.85.dq (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0129183115500114

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