Automatic stenosis detection using SVM from CTA projection images
| Autor: | Chandrabose Aravindan, P. Mirunalini, S. M. Jaisakthi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Computer Networks and Communications business.industry Computer science 020207 software engineering 02 engineering and technology medicine.disease Tracking (particle physics) Support vector machine Stenosis Hardware and Architecture Sliding window protocol 0202 electrical engineering electronic engineering information engineering Media Technology medicine 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Precision and recall Projection (set theory) Rotation (mathematics) Software Information Systems Computed tomography angiography |
| Zdroj: | Multimedia Systems. 25:83-93 |
| ISSN: | 1432-1882 0942-4962 |
| DOI: | 10.1007/s00530-017-0578-1 |
| Popis: | Identification of stenosis in computed tomography angiography (CTA) image of a heart is a challenging task. In this paper, we propose an automated support vector machine (SVM) based approach that detects the branches and stenosis in 2D projection images obtained from different rotation angles of CTA image of a heart. Coronary arteries are segmented from the projection images, centerlines of the arteries are obtained and the presence of stenosis is detected by tracking the arteries along the vessel direction. Tracking is done by sliding overlapping windows in the estimated vessel direction obtained by combining geometric and intensity directions of the vessel. Different SVM models have been built for branch and stenosis detections using geometric and shape based features obtained from the sliding window regions. The proposed system was evaluated in terms of Precision and Recall using CTA images obtained from Billroth Hospitals, Chennai, India, and the results are encouraging. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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