Biomedical signal processing using a new class of wavelets
Zhuoer Shi, Desheng Zhang, Haixiang Wang, Donald Jack Kouri, David K. Hoffman · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
We design a new compactly-supported interpolating wavelet- distributed approximating functional (DAF) wavelet for biomedical signal/image processing. DAF class is a smooth, continuous interpolating function system which is symmetric and fast-decaying. DAF neural networks are designed for time varying electrocardiogram signal filtering. The neural nets use the Hermite-DAF as the basis function and implement a 3- layer structure. DAF wavelets and the corresponding subband filters are constructed for image processing. Edge- enhancement normalization and device-adapted visual group normalization algorithms are presented which sharpen the desired image features without prior knowledge of the spatial characteristics of the images. We design a nonlinear multiscale gradient-stretch method for feature extraction of mammograms. A fractal technique is introduced to characterize microcalcifications in localized regions of breast tissue. We employ a DAF wavelet-based multiscale edge detection and Dijkstra fractal technique is introduced to characterize microcalcifications in localized regions of breast tissue. We employ a DAF wavelet-based multiscale edge detection and Dijkstra fractal technique to identify micro calcification regions, and use a stochastic thresholding method to detect the calcified spots. The combined perceptual techniques produce natural high-quality images based on the human vision system. The underlying technologies significantly facilitate the creation of generic signal processing and computer-aided diagnostic systems. The system is implemented in the JAVA language, which is cross-platform friendly and is facilitated for telemedicine application.