Carotid ultrasound segmentation using radio-frequency derived phase information and gabor filters

Ultrasound image segmentation is a field which has garnered much interest over the years. This is partially due to the complexity of the problem, arising from the lack of contrast between different tissue types which is quite typical of ultrasound images. Recently, segmentation techniques which treat RF signal data have also become popular, particularly with the increasing availability of such data from open-architecture machines. It is believed that RF data provides a rich source of information whose integrity remains intact, as opposed to the loss which occurs through the signal processing chain leading to Brightness Mode Images. Furthermore, phase information contained within RF data has not been studied in much detail, as the nature of the information here appears to be mostly random. In this work however, we show that phase information derived from RF data does elicit structure, characterized by texture patterns. Texture segmentation of this data permits the extraction of rough, but well localized, carotid boundaries. We provide some initial quantitative results, which report the performance of the proposed technique.