Improved Wavelet Algorithm for Medical Image Analysis

The fidelity of a picture that has been exposed to digital processing, such as a contour/texture highlighting procedure or a noise reduction method, can be assessed using two sorts of criteria: objective and subjective, with the two types of criteria occasionally being combined. When the image obtained at the end of the processing is interpreted by man, subjective criteria are the best instrument for evaluating it. The objective criteria are based on the pixel-by-pixel difference between the original and reconstructed image and ensure that the image quality observed by a human viewer is accurately approximated. The pixel-by-pixel changes may also be weighted according to the sensitivity of the human visual system when evaluating the fidelity of a rebuilt (reconstructed) image. The topic of improving medical images is especially essential in assisted diagnosis, which aims to provide clinicians with as much information as possible to help them diagnose disorders. We proposed a technique for reconstructing the contours in images that uses a modified Wiener filter in the wavelet domain and a nonlinear cellular network that is useful both to improve the contrast of its contours and to minimise noise, given that this information must be provided in real time. Medical applications require real-time operation in addition to improved imaging, and this requirement inspired the development of the approach described below, which is based on the modified Wiener filter and nonlinear cellular networks.

Author(S) Details

Catalin Dumitrescu
University Politehnica of Bucharest, 060042 Bucharest, Romania.

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