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Purpose: Bifurcations, shadows and echogenic plaques usually affect proper segmentation of the vessel wall. Thus, identification of these morphological structures is an advisable step when developing segmentation techniques, which have been dealing with this issue by using different features and methods in the past. The aim of this work is to develop a simultaneous classification method for IVUS image sectors into bifurcations, shadows, echogenic plaques and normal, as an intermediate step for the arterial wall segmentation. Methods: A 22-dimensional feature vector, mainly composed by current existing methods, is computed for each column in the polar image. To deal with this multiclass classification problem, Random Forest (RF) is used as classifier. Due to the high skewness of the problem, RFs are successively trained by resampling the training data, specifically the majority class. Results: Fscore reaches 0.62, when the RF is trained with 15% of the normal samples of the training set. Thresholds found in the RF are close to the previously reported values for the features in the literature. Conclusion: Random Forest demonstrates good performance to classify morphological structures in IVUS. Random undersampling for training was useful to deal with the imbalanced data, and to manage the trade-off between precision and recall of minority classes. However, better features must be developed to improve the classification of the structures, specially in the case of the echogenic plaque. |
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CITATIONS
Cited by 5 scholarly publications.
Image segmentation
Intravascular ultrasound
Lithium
Arteries
Image classification
Image filtering
Image processing
