On the assessment of texture feature descriptors in intravascular ultrasound images: a boosting approach to a feasible plaque classification.
Intravascular ultrasound images represent a unique tool to guide interventional coronary procedures, this technique allows to supervise the cross-sectional locations of the vessel morphology and to provide quantitative and qualitative information about the causes and severity of coronary diseases. At the moment, the automatic extraction of this kind of information is performed without taking into account the basic signal principles that guide the process of image generation. In this chapter, we overview the main physical principles and factors that affect the IVUS generation; we propose a simple physics-based approach for IVUS image simulation that is defined as a discrete representation of the tissue by individual scatterrers elements with given spatial distribution and backscattering differential cross sections. In order to generate the physical model that allows to construct synthetic IVUS images, we analyze the process of pulse emission, transmission and reception of the ultrasound signal as well as its interaction with the different tissues scatterrers of the simulated artery. In order to obtain the 3D synthetic image sequences we involve the dynamic behavior of the heart/arteries and the catheter movement in the image generation model. Having an image formation model allows to study rhe physics parameters that participate during the image generation and to achieve a better understanding and robust interpreting of IVUS image structures. Moreover, this model allows to comprehend, simulate and solve several limitations of IVUS sequences, to extract important image parameters to be taken into account when developing robust image processing algorithms as well as to construct wide synthetic image sequence databases in order to validate different image processing techniques.
Pujol
Oriol
O
Centre de Visió per Computador, Edifici O, Campus UAB, 08193 Bellaterra, Barcelona, Spain.
Radeva
Petia
P
D000465:Algorithms; D003324:Coronary Artery Disease; D003430:Cross-Sectional Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015203:Reproducibility of Results; D018084:Ultrasonography, Interventional