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Updated: Jan 20, 2026
Flying Insect Detection and Classification with Inexpensive Sensors
Published on: October 15, 2014
A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents
Su Yang1, Hyuck-Jun Yoon2, Seyed Jamaleddin Mostafavi Yazdi1
1Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea.
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.
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