Using wavelets on denoising infrared medical images

This work presents the conclusions of an experimental study that intends to find the best procedure for reducing the noise of medium resolution infrared images. The goal is to find a good scheme for an image database suitable for use in developing a system to aid breast disease diagnostics. In particular, to use infrared images in the screening and postoperative follow-up in the UFF university hospital, and to combine this with other types of image based diagnoses. Seven wavelet types (Biorthogonal, Coiflets, Daubechies, Haar, Meyer, Reverse Biorthogonal and Symmlets) with various vanishing moments (such as Symmlets, where this number goes from 2 to 28, Daubechies from 1 to 45 and Coiflets 1 to 5) comprising a total of 108 different variations of wavelet functions are compared in a denoising scheme to explore their difference with respect to image quality. Three groups of Additive White Gaussian Noise levels (σ = 5, 25 and 50) are used to evaluate the relations among the approaches to threshold the wavelet coefficient (hard or soft), and the image quality after transformation-denoising-storage-decompression. Levels of decomposition are investigated in a new thresholding scheme, where the decision about the coefficient to be eliminated considers all variation, aiming for the best quality of reconstruction. Eight images of the same type and resolution are used in order to find the mean, median, range and standard deviation of the 432 combinations for each level of noise. Moreover, three evaluators (Normalized Cross-Correlation, Signal to Noise Ratio and Root Mean Squared Error) are considered for recommendation of the best possible combination of parameters.