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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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 classif...
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