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

Abstract

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.