What is it about?
The proposed neural network filter weights are trained with the help of heuristic technique i.e. teaching learning-based optimization (TLBO) technique. It is a nonlinear adaptive filter and helps in removing a specific or mixed type of noise.
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Why is it important?
The proposed work simple in design as well as implementation because it uses single layer neural network i.e. FLANN and for improving the performance level of recently developed TLBO is applied to update the weights. Overall, developed filter improve the accuracy level and not only suppress the dominant rician noise but also able to suppress Gaussian and impulse noise. Hence, TLBO-FLANN filter avoids the problem of selecting a particular algorithm for specific noise.
Perspectives
The developed filter has to test with other medical images and need to improve efficiency level.
Manish Kumar
birla institute of technology
Read the Original
This page is a summary of: Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image, Bio-Medical Materials and Engineering, November 2017, IOS Press,
DOI: 10.3233/bme-171702.
You can read the full text:
Resources
Jaya-FLANN image filter
The noise which degrades the quality of Ultra Sound (US) images may not be of a unique type. Instead, it could be speckle noise inherent in US, impulse noise produced by switching circuits or Gaussian noise getting super-imposed during transmission. When noises of multiple origins and characteristics are present in the image, denoising becomes a difficult task because most of the existing filters are suitable for particular kind of noise. This paper presents a novel adaptive Jaya based functional link artificial neural network (Jaya-FLANN) filter for suppressing different noise present in ultrasound (US) images. Jaya is the optimization algorithm employed to assist in updating weights of FLANN. The target function for Jaya is the minimum error between noisy and contextual pixels of reference images. Compared to Wiener, Multi-Layer Perceptron (MLP), Cat Swarm Optimization based FLANN (CSOFLANN) and Particle Swarm Optimization based FLANN (PSO-FLANN), Jaya-FLANN filter is observed to be superior in terms of Peak Signal to Noise Ratio (PSNR), computational time.
CSO-FLANN Filter
Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN) to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN) and the Cat Swarm Optimization (CSO) is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR), have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.
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