Ms. Carmina D. L. Nascimento Porto Profile

Carmina D. L. Nascimento Porto

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Proceedings Article | 3 March 2017 Paper

Classification of bifurcations regions in IVOCT images using support vector machine and artificial neural network models

C. D. Porto, C. F. Costa Filho, M. M. Macedo, M. Gutierrez, M. G. Costa

Proceedings Volume 10134, 101344D (2017) https://doi.org/10.1117/12.2254470

KEYWORDS: Optical coherence tomography, Neural networks, Feature selection, Image classification, Artificial neural networks, Medical imaging, Principal component analysis, Laser Doppler velocimetry, Blood, Intravascular ultrasound, Heart

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Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.

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