Bai W, Oktay O, Rueckert D (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: Springer Verlag
Book Volume: 9534
Pages Range: 140-145
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783319287119
DOI: 10.1007/978-3-319-28712-6_15
Myocardial infarction changes both the shape and motion of the heart. In this work, cardiac shape and motion features are extracted from shape models at ED and ES phases and combined to train a SVM classifier between myocardial infarcted cases and asymptomatic cases. Shape features are characterised by PCA coefficients of a shape model, whereas motion features include wall thickening and wall motion. Evaluated on the STACOM 2015 challenge dataset, the proposed method achieves a high accuracy of 97.5% for classification, which shows that shape and motion features can be useful biomarkers for myocardial infarction, which provide complementary information to late-gadolinium MR assessment.
APA:
Bai, W., Oktay, O., & Rueckert, D. (2016). Classification of myocardial infarcted patients by combining shape and motion features. In Kawal Rhode, Oscar Camara, Alistair Young, Tommaso Mansi, Maxime Sermesant, Mihaela Pop (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 140-145). Munich, DEU: Springer Verlag.
MLA:
Bai, Wenjia, Ozan Oktay, and Daniel Rueckert. "Classification of myocardial infarcted patients by combining shape and motion features." Proceedings of the 6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015, Munich, DEU Ed. Kawal Rhode, Oscar Camara, Alistair Young, Tommaso Mansi, Maxime Sermesant, Mihaela Pop, Springer Verlag, 2016. 140-145.
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