New Approaches for Plaque Component Analysis in Intravascular Ultrasound (IVUS) Images
Heart attack and stroke are the major causes of human death and atherosclerotic plaques are the most common effect of cardiovascular disease. Intravascular ultrasound (IVUS), a diagnostic imaging technique, offers a unique view of the morphology of the arterial plaque and displays the morphological and histological properties of a cross section of the vessel. Limitations of the gray-scale IVUS manual plaque assessment have led to the development of quantitative techniques for analysis of characteristics of plaque components.
In vivo plaque characterization with the so-called Virtual Histology (VH)-IVUS, which is based on the ultrasound RF signal processing, is widely available for atherosclerosis plaque characterization in IVUS images. However, it suffers from a poor longitudinal resolution due to the ECG-gated acquisition. The focus of this chapter is to provide effective methods for image-based vessel plaque characterization via IVUS image analysis to overcome the limitations of current techniques. The proposed algorithms are also applicable to the large amount of the IVUS image sequences obtained from patients in the past, where there is no access to the corresponding radiofrequency (RF) data. Since the proposed method is applicable to all IVUS frames of the heart cycle, it outperforms the longitudinal resolution of the so-called VH method.
The procedures of analyzing gray-scale IVUS images can be divided into two separated aspects: (1) detecting the vessel borders to extract the region called “plaque area” and (2) characterizing the atherosclerosis plaque composition. The latter one consists of two main steps: in the first one, known as feature extraction, the plaque area of the cross-sectional IVUS image is modeled using appropriate features.
The second step based on learning techniques assists the classifier in distinguishing different classes more precisely and in assigning labels to each of the samples generated by feature extraction within the first step.
In vivo and ex vivo validation procedures were used, where the results proved the efficiency of the proposed algorithms for vessel plaque characterization via IVUS images. A graphic user interface (GUI) is designed as an effective image processing tool which enables cardiologists with a complete IVUS image processing tool from border detection to plaque characterization. The algorithms developed within this thesis lead to the enhancement of the longitudinal resolution of plaque composition analysis.
KeywordsSupport Vector Machine Local Binary Pattern Support Vector Machine Classifier Feature Extraction Method Necrotic Core
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