Background:
Various kind of medical imaging modalities are available for providing
noninvasive view and for analyzing any pathological symptoms of human beings. Different noise
may appear in those modalities at the time of acquisition, transmission, scanning, or at the time of
storing. The removal of noises from the digital medical images without losing any inherent features
is always considered a challenging task because a successful diagnosis relies on them. Numerous
techniques have been proposed to fulfill this objective, and each having their own benefits and
limitations.
Discussion:
In this comprehensive review article, more than 65 research articles are investigated to
illustrate the applications of Artificial Neural Networks (ANN) in the field of biomedical image
denoising. In particular, the zest of this article is to highlight the hybridized filtering model using
nature-inspired algorithms and artificial neural networks for suppression of noise. Various other
techniques, such as fixed filter, linear adaptive filters and gradient descent learning based neural
network filter are also included.
Conclusion:
This article envisages how to train ANN using derivative free nature-inspired algorithms,
and its performance in various medical images modalities and noise conditions.
Recognition of 3-dimensional medical images of the lungs, pulmonary vessels and bronchial trees by artificial neural networks
