Untitled

Abstract

Aim: Image fusion has been a widely used application in the field of medical diagnosis. Hence, a very little amount of literature has been found out on Bone and vascular image fusion. Wavelet has been a revolutionary tool for the better representation and reconstruction of images. This article aims at testing the performance of the various wavelet families on the fusion of bone vessel fusion. Materials and Methods: A mask image displaying the osseous information and a digital subtraction angiography (DSA) image containing the vascular details are chosen to test the performance of various wavelet families on their fusion. Results and Discussions: The Daubechies Wavelet works best among all the families, but wavelets, in general, are not able to perform well on image fusion as a low amount of Q AB/F has been achieved. Conclusion: Although the wavelets have been used widely in the context of image fusion they are not working well with DSA and mask image fusion. We have portrayed the limitation of wavelet on bone vascular image fusion.

Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces

“…There are various methods used in all these stages including the feature extraction stage. However, the discrete wavelet transform (DWT) method, which is known to be effective especially in non-stationary signals during the feature extraction stage, gives relatively better results in EEG-based BCI studies [8][9][10][11][12][13][14][15][16][17], compressing the grayscale images [18], filtering angiopraphic images [19], recognizing isolated spoken words [20,21], diagnozing epileptic patients [22,23], determining eye blinks [24], understanding the meditation [25].…”