Intravascular Ultrasound Image Composite Segmentation using Ensemble Gabor-spatial Features

One of the most widespread conditions that also have a high mortality rate is coronary artery disease (CAD). The accurate identification of aortic stenosis can lead to more effective therapy, which in turn can reduce the risk of dying from the condition. Images obtained using intravascular ultrasonography (IVUS) can be analyzed to determine the status of plaque that is located between the lumen and the media adventitia within the artery. To accomplish this task, IVUS's components need to be identified. In this paper, the approach to segment IVUS's component consisting of lumen, media, and adventitia is proposed. Support Vector Machine classification model is trained using extracted spatial features. Gray level cooccurrence matrix and Gabor features are extracted from polar space transformed images. The result of the proposed approach reaches 0.82 of the Jaccard Index (JM), 10.87 of Hausdorff Distance (HD), and 1.89 of Percentage Area Difference (PAD). Moreover, the classification model is evaluated using a confusion matrix. The performance of the classification is 0.89 accuracy, 0.921 precision, and 0.944 recall.