Three-Dimensional Isotropic Wavelets for Post-Acquisitional Extraction of Latent Images of Atherosclerotic Plaque Components from Micro-Computed Tomography of Human Coronary Arteries

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

“…Specificity is the empirical conditional probability, per specimen, that a lipid lesion occurring in the reconstructed volume is a lipid lesion verified by histology. Details of this study and statistical analysis of the results may be found in [4]. For further testing, the arterial specimens were chemically decalcified and imaged again before sectioning for histology.…”

“…Our initial studies found that image processing via fast isotropic wavelet algorithms permits threedimensional, high-resolution digital discrimination between calcific deposits, lipid-rich tissue that is lightly calcified or even non-calcified, and the surrounding lipid-poor (fibrous) tissue [4,27]. Figure 3 shows a comparison of the unprocessed data obtained from scanning a coronary artery specimen with results that are based on textural analysis.…”

“…[1][2][3]. Previously, we investigated the benefits of texture-based image analysis of high-resolution CT scans for the post-acquisitional identification of soft tissue lesions [4]. The present paper explains the mathematical structure of the underlying image processing algorithm and shows results from the analysis of CT and flat-panel CT scans as well as preliminary results for a 64-slice CT scanner.…”

The geometry and the analytic properties of isotropic multiresolution analysis

“…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.…”