Vessel enhancement in digital X-ray angiographic sequences by temporal statistical learning
| Autor: | Emanuele Trucco, András Lassó |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Health Informatics Pattern Recognition Automated Artificial Intelligence Humans Radiology Nuclear Medicine and imaging Computer vision Mathematics Models Statistical Radiological and Ultrasound Technology Pixel business.industry Matched filter Angiography Subtraction Pattern recognition Filter (signal processing) Computer Graphics and Computer-Aided Design Focus stacking Radiographic Image Enhancement Support vector machine Computer Science::Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition Artificial intelligence business Algorithms Linear filter |
| Zdroj: | Computerized Medical Imaging and Graphics. 29:343-355 |
| ISSN: | 0895-6111 |
| Popis: | In this paper, we present a vessel enhancement method, SVM temporal filtering (STF), for X-ray angiographic (XA) images using Support Vector Machine (SVM). We show that the linear SVM applied to vessel enhancement can be regarded as a matched linear filter optimizing the contrast-to-noise ratio in XA images. We propose a non-linear kernel function for the SVM leading to good enhancement with noisy, varying grey-level dynamics at vessel pixels. One key advantage over the matched filters is that an optimal filter is learnt from images, not estimated at design stage. Results on clinical XA images show that learning-based enhancement achieves better results compared to simple subtraction and other image stacking methods. |
| Databáze: | OpenAIRE |
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