Although Virtual Histology (VH) is the in-vivo gold standard for atherosclerosis plaque characterization in IVUS
images, it suffers from a poor longitudinal resolution due to ECG-gating. In this paper, we propose an image-based approach to overcome this limitation. Since each tissue have different echogenic characteristics, they show
in IVUS images different local frequency components. By using Redundant Wavelet Packet Transform (RWPT),
IVUS images are decomposed in multiple sub-band images. To encode the textural statistics of each resulting
image, 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 (LDB) algorithm in combination with Fisher's criterion. A structure of weighted multi-class SVM permits the classification of the extracted feature vectors into three tissue classes, namely fibro-fatty, necrotic core and dense calcified tissues. Results shows the superiority of our approach with an overall accuracy of 72% in comparison to methods based on Local Binary Pattern and Co-occurrence, which respectively give accuracy rates of 70% and 71%.