Energy Domain Noise Reduction Technique

A versatile wavelet domain noise filtration technique for medical imaging abstract.

Energy domain noise reduction technique. Noise reduction of up to 59 28 7 of 65 7 was achieved for de virtual monoenergetic images by using an energy domain noise reduction technique. Noise reduction techniques exist for audio and images. A single parameter can be used to balance the preservation of expert dependent relevant details against the degree of noise reduction.

The noise model is included in the ideal observer detectability for performance evaluation of the noise reduction techniques. Energy domain noise reduction has been successfully applied to the full range of dual or multi energy processing techniques including the generation of blended images linear or nonlinear combination of the low and high energy images virtual monoenergetic images or material specific images 44 48. Dual energy image noise with and without including the effect of anatomical noise in noise reduction technique analysis is reported.

Instead of fitting silencers it is often possible to achieve a 10 20 db reduction in airborne noise from a duct or opening by lining the last bend in the ductwork with acoustic. Faraday cage a faraday cage enclosing a circuit can be used to isolate the circuit from external noise sources. For the commercial virtual monoenergetic images the maximum iodine cnr was achieved at 70 kev and was 18 6 16 6 and 10 8 for the 30 35 and 45 cm phantoms.

All signal processing devices both analog and digital have traits that make them susceptible to noise. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert. The 60 kev virtual monoenergetic image reconstruction plus deep learning images showed low noise no.

Cnr of virtual monoenergetic images was also compared with that of single energy images acquired with 80 100 120 and 140 kv. The noise source is where the vibratory mechanical energy originates as. Any noise problem may be described in terms of a source a transmission path and a receiver in this context a worker and noise control may take the form of altering any one or all of these elements.

Noise reduction algorithms tend to alter signals to a greater or lesser degree. It can be applied before material decomposition on the individual energy specific images and after material decomposition on the material specific or virtual monoenergetic images. We propose a robust wavelet domain method for noise filtering in medical images.