Wavelet image interpolation (WII): a wavelet-based approach to enhancement of digital mammography images

Authors:
Gordana Derado;F. DuBois Bowman;Rajan Patel;Mary Newell;Brani Vidakovic
Affiliations:
Department of Biostatistics, Emory University, Atlanta, GA;Department of Biostatistics, Emory University, Atlanta, GA;Department of Biostatistics, Emory University, Atlanta, GA and Amgen Inc., Thousand Oaks, CA;Department of Radiology, Emory University, Atlanta, GA;Department of Biostatistics, Emory University, Atlanta, GA and Georgia Institute of Technology, Atlanta, GA
Venue:
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Year:
2007

Citing 3
Cited 4

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Coherence of multiscale features for enhancement of digital mammograms

IEEE Transactions on Information Technology in Biomedicine
Isotropic polyharmonic B-splines: scaling functions and wavelets

IEEE Transactions on Image Processing

Contrast enhancement for image by WNN and GA combining PSNR with information entropy

Fuzzy Optimization and Decision Making
Adaptive typhoon cloud image enhancement using genetic algorithm and non-linear gain operation in undecimated wavelet domain

Engineering Applications of Artificial Intelligence
Wavelet-based 3-D multifractal spectrum with applications in breast MRI images

ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Research on 3d object rounding photography systems and technology

ICICA'12 Proceedings of the Third international conference on Information Computing and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

Cancer detection using mammography focuses, in part, on characteristics of tiny microcalcifications, including the number, size, and spatial arrangement of the microcalcifications, as well as morphological features of individual microcalcifications. We have developed state-of-the-art wavelet-based methods to enhance the resolution of microcalcifications visible on digital mammograms, aimed at improving the specificity of breast cancer diagnoses. In our research, we develop, refine, and evaluate a Wavelet Image Interpolation (WII) procedure and create accompanying software to implement it. 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. The construction of detail coefficients is supervised by the observed image and innate regular scaling assessed by a statistical model. We found that our proposed procedure is efficient and useful in capturing relevant clinical information in the context of digital mammographic imaging. Our proposed methodology was tested by an experienced radiologist using 40 images from the University of South Florida Digital Database for Screening Mammography (DDSM).