Genetic fuzzy rule-based classification systems for tissue characterization of intravascular ultrasound images

This paper proposes the application of a genetic fuzzy rule-based classification system (GFRBCS) for tissue characterization of intravascular ultrasound (IVUS) images. The presented approach follows the IVUS Virtual Histology (IVUS-VH) plaque characterization technique, whereby the plaque region is classified into four primary tissue types, namely, calcium, necrotic core, fibrous and fibro-fatty. In order to increase the discrimination between the classes, a rich set of textural features is derived at different scales, including first-order statistics, gray-level co-occurrence matrices, run-lengths, wavelets, local binary patterns (LBP) and local indicators of spatial association (LISA) features. The employed fuzzy classifier effectively exploits the provided information, producing accurate and highly interpretable classification models. The extensive experimental analysis performed highlights the advantages of the proposed scheme against existing methods of the literature.