Advanced Image Processing for Analytics in Biomedicine and Bioscience

Abstract

All medical imaging systems suffer from the effects of acquisition noise, channel noise, and fading. When decisions relevant to these image data are taken, any deviation from real values could affect the decisions made. Additionally, detecting anomalies from image data requires special processing to go behind the surface data. We have developed computationally low power, low bandwidth, and low cost filters (DMAW) that will remove the noise, compress the image, and decompose the image so that a decision can be made by looking at different layers of image data. This wavelet-based method is guaranteed to converge to a stationary point for both uncorrelated and correlated image data. Presented here is the theoretical background with examples showing the performance and merits of this novel approach compared to other alternatives.

The Altes Family of Log-Periodic Chirplets and the Hyperbolic Chirplet Transform

“…Their recommendation is that such adaptive-TFDs should have the property of scale-invariance, but they implement matching pursuit using a Gaussian windowed Gabor tiling which does not. The work of [32] proposes an adaptive Meyer wavelet as an improvement on the short-time Fourier transform (STFT) for processing medical signals containing chirps. Similarly, in [33], the authors suggest that frequency-swept ultrasonic sonar chirping could be a useful method for non-contact bio-mechanical measurement, in this case, of the chest movements associated with breathing.…”