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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

A. Taki, O. Pauly, S.K. Setarehdan, G. Unal, N. Navab
IVUS-based histology of atherosclerotic plaques: improving longitudinal resolution
SPIE Medical Imaging, San Diego, California, USA, 13-18 February 2010 (bib)

Although Virtual Histology is used for atherosclerosis plaque characterization in IVUS images, it suffers from a poor longitudinal resolution due to ECG-gating. By using Redundant Wavelet Packet Transform, IVUS images are decomposed in multiple sub-band images. Run-length features are extracted from the neighborhood centered on each pixel. To provide the best discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant Bases algorithm. SVM classifier is used for the classification of the extracted feature vectors into three tissue classes. Results show our approach with an overall accuracy of 72% in comparison to Local Binary Pattern and Co-occurrence. Although Virtual Histology is used for atherosclerosis plaque characterization in IVUS images, it suffers from a poor longitudinal resolution due to ECG-gating. By using Redundant Wavelet Packet Transform, IVUS images are decomposed in multiple sub-band images. Run-length features are extracted from the neighborhood centered on each pixel. To provide the best discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant Bases algorithm. SVM classifier is used for the classification of the extracted feature vectors into three tissue classes. Results show our approach with an overall accuracy of 72% in comparison to Local Binary Pattern and Co-occurrence.
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Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

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