Automatic detection of luminal borders in IVUS images by magnitude-phase histograms of complex brushlet coefficients

We present a new technique to delineate lumen borders in intravascular ultrasound (IVUS) volumes of images acquired with a high-frequency Volcano (Rancho Cordova, CA) 45MHz transducer. Our technique relies on projection of IVUS sub-volumes onto orthogonal directional brushlet functions. Through selective projection of IVUS sub-volumes images and their Fourier transforms, tissue-specific backscattered magnitudes and phases identified within brushlet coefficients. We take advantage of such characteristics and construct 2.5-dimensional (2.5-D) magnitudes-phase histograms of coefficients in the transformed complex brushlet domain that contain distinct peaks corresponding to blood and non-blood regions. We exploit these peaks to mask out coefficients that represent blood regions and ultimately detect the luminal border after spatial regularization employing a parametric deformable model. We quantify our results by comparing them to manually traced borders by an expert on 2 datasets, containing 108 frames. We show that our approach is well suited for isolating coherent (i.e. plaque) structures from incoherent (i.e. blood) ones in IVUS pullbacks and detecting the lumen border, a challenging problem particularly in images acquired with high frequency transducers.