Su Yang1, Hyuck-Jun Yoon2, Seyed Jamaleddin Mostafavi Yazdi1, Jong-Ha Lee1. 1. Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea. 2. Department of Internal Medicine, School of Medicine, Keimyung University, Daegu, South Korea.
Abstract
BACKGROUND: Clinically, irregular protrusions and blockages after stent deployment can lead to significant adverse outcomes such as thrombotic reocclusion or restenosis. In this study, we propose a novel fully automated method for irregular lumen segmentation and normal/abnormal lumen classification. METHODS: The proposed method consists of a lumen segmentation, feature extraction, and lumen classification. In total, 92 features were extracted to classify normal/abnormal lumen. The lumen classification method is a combination of supervised learning algorithm and feature selection that is a partition-membership filter method. RESULTS: As the results, our proposed lumen segmentation method obtained the average of dice similarity coefficient (DSC) and the accuracy of proposed features and the random forest (RF) for normal/abnormal lumen classification as 97.6% and 98.2%, respectively. CONCLUSIONS: Therefore, we can lead to better understanding of the overall vascular status and help to determine cardiovascular diagnosis.
BACKGROUND: Clinically, irregular protrusions and blockages after stent deployment can lead to significant adverse outcomes such as thrombotic reocclusion or restenosis. In this study, we propose a novel fully automated method for irregular lumen segmentation and normal/abnormal lumen classification. METHODS: The proposed method consists of a lumen segmentation, feature extraction, and lumen classification. In total, 92 features were extracted to classify normal/abnormal lumen. The lumen classification method is a combination of supervised learning algorithm and feature selection that is a partition-membership filter method. RESULTS: As the results, our proposed lumen segmentation method obtained the average of dice similarity coefficient (DSC) and the accuracy of proposed features and the random forest (RF) for normal/abnormal lumen classification as 97.6% and 98.2%, respectively. CONCLUSIONS: Therefore, we can lead to better understanding of the overall vascular status and help to determine cardiovascular diagnosis.