Applications of Multiscale Overcomplete Wavelet-Based Representations in Intravascular Ultrasound (IVUS) Images

The importance of atherosclerotic disease in coronary artery has been a subject of study for many researchers in the past decade. In brief, the aim is to understand progression of such a disease, detect plaques at risks (vulnerable plaques), and treat them selectively to prevent mortality and immobility. Consequently, several imaging modalities have been developed and among them intravascular ultrasound (IVUS) has been of particular interest since it provides useful information about tissues microstructures and images with sufficient penetration as well as resolution.

[1]  E. Tuzcu,et al.  Coronary Plaque Classification With Intravascular Ultrasound Radiofrequency Data Analysis , 2002, Circulation.

[2]  T S Ikonen,et al.  Feasibility of in vivo intravascular ultrasound tissue characterization in the detection of early vascular transplant rejection. , 1999, Circulation.

[3]  Elsa D. Angelini,et al.  Lumen Border Detection of Intravascular Ultrasound via Denoising of Directional Wavelet Representations , 2009, FIMH.

[4]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Walter L. Henry,et al.  Intravascular Ultrasound Imaging of Human Coronary Arteries In Vivo: Analysis of Tissue Characterizations With Comparison to In Vitro Histological Specimens , 1991, Circulation.

[6]  Petia Radeva,et al.  Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration , 2007, CAIP.

[7]  Tong Fang,et al.  Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images , 2008, IEEE Transactions on Information Technology in Biomedicine.

[8]  M. Arai,et al.  Noninvasive quantitative tissue characterization and two-dimensional color-coded map of human atherosclerotic lesions using ultrasound integrated backscatter: comparison between histology and integrated backscatter images. , 2001, Journal of the American College of Cardiology.

[9]  Elsa D. Angelini,et al.  Classification of blood regions in IVUS images using three dimensional brushlet expansions , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Elisa E. Konofagou,et al.  Challenges in Atherosclerotic Plaque Characterization With Intravascular Ultrasound (IVUS): From Data Collection to Classification , 2008, IEEE Transactions on Information Technology in Biomedicine.

[11]  N. Obuchowski,et al.  Assessing spectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data. , 2001, Ultrasound in Medicine and Biology.

[12]  Elsa D. Angelini,et al.  LV volume quantification via spatiotemporal analysis of real-time 3-D echocardiography , 2001, IEEE Transactions on Medical Imaging.

[13]  Elisa E. Konofagou,et al.  Texture-driven coronary artery plaque characterization using wavelet packet signatures , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[14]  Olivier Rioul,et al.  A discrete-time multiresolution theory , 1993, IEEE Trans. Signal Process..

[15]  Nassir Navab,et al.  Automatic segmentation of calcified plaques and vessel borders in IVUS images , 2008, International Journal of Computer Assisted Radiology and Surgery.

[16]  Milan Sonka,et al.  Tissue characterization in intravascular ultrasound images , 1998, IEEE Transactions on Medical Imaging.

[17]  G. Weiss,et al.  Local sine and cosine bases of Coifman and Meyer and the construction of smooth wavelets , 1993 .

[18]  M. Leon,et al.  Characterization of atherosclerotic lesions by intravascular ultrasound: possible role in unstable coronary syndromes and in interventional therapeutic procedures. , 1991, The American journal of cardiology.

[19]  Scott Rickard,et al.  Blind separation of speech mixtures via time-frequency masking , 2004, IEEE Transactions on Signal Processing.

[20]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[21]  Ronald R. Coifman,et al.  Brushlets: A Tool for Directional Image Analysis and Image Compression , 1997 .

[22]  Sergio Escalera,et al.  Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes , 2009, J. Signal Process. Syst..

[23]  Elsa D. Angelini,et al.  Surface Function Actives , 2009, J. Vis. Commun. Image Represent..

[24]  Petia Radeva,et al.  Segmentation of artery wall in coronary IVUS images: a probabilistic approach , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[25]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[26]  Jian Fan,et al.  Frame representations for texture segmentation , 1996, IEEE Trans. Image Process..

[27]  Nassir Navab,et al.  A new method for characterization of coronary plaque composition via IVUS images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[28]  V J Caiozzo,et al.  Are soft echoes really soft? Intravascular ultrasound assessment of mechanical properties in human atherosclerotic tissue. , 1997, American heart journal.

[29]  Olivier Basset,et al.  Spatial anisotropic diffusion and local time correlation applied to segmentation of vessels in ultrasound image sequences , 2003, IEEE Symposium on Ultrasonics, 2003.

[30]  Milan Sonka,et al.  Segmentation of intravascular ultrasound images: a knowledge-based approach , 1995, IEEE Trans. Medical Imaging.

[31]  J. Thomas,et al.  Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images. , 1999, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[32]  Jean Meunier,et al.  Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions , 2006, IEEE Transactions on Medical Imaging.

[33]  Amin Katouzian,et al.  Challenges in tissue characterization from backscattered intravascular ultrasound signals , 2007, SPIE Medical Imaging.

[34]  Jacob Beck,et al.  Spatial frequency channels and perceptual grouping in texture segregation , 1987, Comput. Vis. Graph. Image Process..

[35]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[36]  P. Fitzgerald,et al.  Feasibility of a Novel Blood Noise Reduction Algorithm to Enhance Reproducibility of Ultra–High-Frequency Intravascular Ultrasound Images , 2000, Circulation.

[37]  A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Petia Radeva,et al.  A Deterministic-Statistic Adventitia Detection in IVUS Images , 2005, FIMH.

[39]  C. Chui.  Wavelets: A Tutorial in Theory and Applications , 1992 .