Reconstructing ivus images for an accurate tissue classification

Plaque rupture in coronary vessels is one of the principal causes of sudden death in western societies. Reliable diagnostic tools are of great interest for physicians in order to detect and quantify vulnerable plaque in order to develop an effective treatment. To achieve this, a tissue classification must be performed. Intravascular Ultrasound (IVUS) represents a powerful technique to explore the vessel walls and to observe its morphology and histological properties. In this paper, we propose a method to reconstruct IVUS images from the raw Radio Frequency (RF) data coming from the ultrasound catheter. This framework offers a normalization scheme to compare accurately different patient studies. Then, an automatic tissue classification based on the texture analysis of these images and the use of Adapting Boosting (AdaBoost) learning technique combined with Error Correcting Output Codes (ECOC) is presented. In this study, 9 in-vivo cases are reconstructed with 7 different parameter set. This method improves the classification rate based on images, yielding a 91% of well-detected tissue using the best parameter set. It is also reduced the inter-patient variability compared with the analysis of DICOM images, which are obtained from the commercial equipment.