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Diagnostic Decision Making using High Frequency Bioresponses and Medical Imaging
VOCs Detecting Breast Cancer
Multifractal Analysis of H-NMR
In NMR spectra, a wealth of information is ignored. From the resolution of tens of thousands of metabolites, traditional analysis focuses on a few peaks. The idea is to look at the spectrum as a (multi)fractal and summarize (multi)fractal properties.
About BESTAAims
The center aims are to promote research and consulting in all aspects involving the planning of statistical experiments and statistical modeling of results, with an emphasis on biomedical data.
Members
Melinda Higgins Sky Lee Xavier Le Faucheur
Brani Vidakovic
Hin Kyeol Woo Lucy Petrova Karan Raturi
Contact UsBESTA - Center for Bioengineering Statistics Wallace Coulter Department of Biomedical Engineering Georgia Institute of Technology 1213 Whitaker Building. Atlanta, GA 30332
Wavelets on SurfacesXavier Le Faucheur (joint with Delphine Nain, Allen Tannenbaum, and Brani Vidakovic)
Preliminary results published in SPIE 6763, 2007
Project with Dean, Park, and Ziegler (Div. Pulmonary and Critical Care Med. Emory).
Preliminary results published Journal of Data Science 2008
Aim: To connect fractality descriptors to measures of sulfur-amino acid (SAA) deficiency (cysteine)
Wavelet-based 3-D MFS in BMRI
• Wavelet Image Interpolation (WII) is a wavelet-based approach to enhancement of digital mammography images.
• WII involves the application of
an inverse wavelet transformation to a coarse or degraded image and constructed detail coefficients to produce an enhanced higher resolution image.
Procedure
1. One performs k wavelet decomposition steps on empty image. The transform is linear and the resulting smooth and detail sub-matrices are all zero-matrices.
2. The degraded image from a digital mammogram is inserted into the position of the smooth matrix containing zeros.
3. The object in 2 is back-transformed by k steps.
This process increases the resolution of the degraded image and contains 4k times the number of pixels in the original input.
Project with CBIS and Emory (Winship Cancer Institute)
Communicated at ISBRA 2008, and part of NIH grant proposal 2008
Aim: To classify BMRI images to benign and malignant using wavelet-based multifractal spectrum (MFS) of the image background
Description of Data
Case Control
Wavelet spectrum/Analysis
• The extended three dimensional concept of wavelet-based multifractal spectrum is used in classification of BMRI
Project with Charlene Bayer (GTRI), Sheryl Gabram-Mendola (Winship), and Boris Mizaikoff (University of Ulm)
• Descriptors and realizations of multifractal spectrum
Aim: To diagnose subject with cancer based on the VOC (Volatile Organic Compound) content of their breath
Description of Data: 383 VOCs per subject; 35 subjects (24 controls, 11 cases)
Dimension Reduction/Analysis
• Nonlinear dimension reduction is very discriminative
• Dimension reduction from 383 VOCs to 2 informative components
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Original Shape
Squared Error
Noisy Shape
Recovered Shape after Shrinkage
Inverse Wavelet Transform
Bayesian Wavelet Shrinkage using Shape Features
Encoding using Spherical WaveletsShape signal (x,y,z)
Wavelet Coefficients
Shrunk Wavelet Coefficients
Smooth Shape Signal
Error
Project with Dubois Bowman (RSPH, Emory)
Communicated at ISBRA 2007 and Georgia Cancer Coalition seed grant award
Wavelet Enhancement of Mammograms