Automatic detection of blood versus non-blood regions on intravascular ultrasound (IVUS) images using wavelet packet signatures

Abstract

Intravascular ultrasound (IVUS) has been proven a reliable imaging modality that is widely employed in cardiac interventional procedures. It can provide morphologic as well as pathologic information on the occluded plaques in the coronary arteries. In this paper, we present a new technique using wavelet packet analysis that differentiates between blood and non-blood regions on the IVUS images. We utilized the multi-channel texture segmentation algorithm based on the discrete wavelet packet frames (DWPF). A k-mean clustering algorithm was deployed to partition the extracted textural features into blood and non-blood in an unsupervised fashion. Finally, the geometric and statistical information of the segmented regions was used to estimate the closest set of pixels to the lumen border and a spline curve was fitted to the set. The presented algorithm may be helpful in delineating the lumen border automatically and more reliably prior to the process of plaque characterization, especially with 40 MHz transducers, where appearance of the red blood cells renders the border detection more challenging, even manually. Experimental results are shown and they are quantitatively compared with manually traced borders by an expert. It is concluded that our two dimensional (2-D) algorithm, which is independent of the cardiac and catheter motions performs well in both in-vivo and in-vitro cases.

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

“…The original IVUS data was provided to each author and they provide us with the segmentation results. The methods used for comparison correspond to the methods presented by Unal et (Katouzian et al (2008)). The details of each sequence and the number of segmented frames provided by each group are listed in Table 2 3 .…”

“…Multilevel discrete wavelet frames decomposition was used by Papadogiorgaki et al (Papadogiorgaki et al (2008)) to generate texture information that was used along with the intensity information for contour initialization. Similarly, Katouzian et al (Katouzian et al (2008)) presented a method where texture information was extracted using a discrete wavelet packet transform. Then, pixels were classified as lumen or non-lumen using k-means clustering.…”

An Inverse Scattering Algorithm for the Segmentation of the Luminal Border on Intravascular Ultrasound Data

“…Kawasaki et al [8] proposed another method of tissue classification using the integrated backscatter (IB) parameter. O'Malley et al and Katouzian et al [9,10] explored methods for blood characterization. Mendizabal-Ruiz et al [11] presented a method for the identification of contrast agent.…”

Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data

“…For these images, edge information is not sufficient and therefore, later approaches incorporated prior knowledge using region and global information such as texture [1], gray level variances [2,3], statistical properties of the intensities [4], temporal information (3D segmentation) [5], and discrete wavelet decomposition [6]. Most recent approaches include the use of nonparametric probability densities with global measurements [7], multilevel discrete wavelet frame decomposition [8], discrete wavelet packet transform [9], machine learning classification methods [10], a combination of gray level probability density functions and the intensity gradient [11], linear-filtered gradient vector flow which drives the deformation of a balloon snake [12], and binary morphological object reconstruction [13].…”