Texture‐based tissue characterization for high‐resolution CT scans of coronary arteries

Abstract

SUMMARYWe analyze localized textural consistencies in high-resolution X-ray (computed tomography) CT scans of coronary arteries to identify the appearance of diagnostically relevant changes in tissue. For the efficient and accurate processing of CT volume data, we use fast wavelet algorithms associated with threedimensional isotropic multiresolution wavelets that implement a redundant, frame-based image encoding without directional preference. Our algorithm identifies textural consistencies by correlating coefficients in the wavelet representation.

The geometry and the analytic properties of isotropic multiresolution analysis

“…Recall from Theorem 2.1, that such V 0 are of the form P W Ω , where Ω is a radial set. We prove the following result: [15,57]. In segmentation, the only purpose in transforming the original signal is to improve clustering, i.e.…”

“…astronomical images of objects with a high degree of spherical symmetry e.g., [65], while others use them to construct covariant representations with respect to rigid motions: Among them Fickus and co-workers develop higher-order moments covariant with respect to rigid motions in multidimensions [33,34]. Our approach to isotropic filters and IMRAs originated in [58] and continued in [15,57] and the present paper.…”

“…accentuate the most relevant information inherent in that signal to allow for more accurate segmentation. Details on our IMRA-approach to texture segmentation for tissue discrimination and on the application of our method to x-ray CT 3D-data sets can be found in [15,57] 1 . The IMRA approach has two significant practical advantages.…”

“…The driving application for our study of IMRAs is tissue identification in biomedical imaging [15,37,38,57]. Specifically in our initial work the input 3D-data sets are µCT (micro Computed Tomography) images of arterial specimens.…”

Correspondence between frame shrinkage and high-order nonlinear diffusion

“…The reader is referred to [1,10] for the relationship between PDE diffusion and the bilateral filter, another popular method for image denoising. Recently wavelet frames have been successfully used in noise removal [30], image recovery [7,8], image inpainting/restoration [3,4,5], signal classification [9] and medical image analysis [18,25]. Compared with wavelet systems, the elements in a frame system may be linearly dependent; namely, frames can be redundant.…”