Diagnostic Decision Making using High Frequency Bioresponses and Medical Imaging

1. Diagnostic Decision Making using High Frequency Bioresponses and Medical Imaging Wavelet-based 3-D MFS in BMRI Multifractal Analysis of H-NMR 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) 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 About BESTA Aims 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 BraniVidakovic HinKyeol Woo Lucy Petrova Karan Raturi Contact Us BESTA - Center for Bioengineering Statistics Wallace Coulter Department of Biomedical Engineering Georgia Institute of Technology 1213 Whitaker Building. Atlanta, GA 30332 Description of Data Case Control 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. Wavelet spectrum/Analysis • Descriptors and realizations of multifractal spectrum • The extended three dimensional concept ofwavelet-based multifractal spectrum is used in classification of BMRI Wavelets on Surfaces Wavelet Enhancement of Mammograms Xavier Le Faucheur(joint with Delphine Nain, Allen Tannenbaum, and Brani Vidakovic) Preliminary results published in SPIE 6763, 2007 VOCs Detecting Breast Cancer Project with Dubois Bowman (RSPH, Emory) Communicated at ISBRA 2007 and Georgia Cancer Coalition seed grant award Project with Charlene Bayer (GTRI), Sheryl Gabram-Mendola (Winship), and Boris Mizaikoff (University of Ulm) • 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. 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 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. • Dimension reduction from 383 VOCs to 2 informative components Original Shape Noisy Shape • Nonlinear dimension reduction is very discriminative Recovered Shape after Shrinkage Squared Error