IVUS-based histology of atherosclerotic plaques: improving longitudinal resolution

Abstract

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 imagebased 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%.

Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

“…It selects the optimal set of orthogonal subspaces that can provide maximum dissimilarity information among different classes [13,14]. Up to date, LDB has been applied to deal with real-world classification problems in the areas of audio signal analysis [15,16], physiological signal classification [17,18], and vibration data processing [13,19]. From these applications, it can be seen that the results of LDB algorithm for a given dataset are driven by the nature of the dataset and the dissimilarity measures.…”