Abstract
Medical image diagnosis plays an important role in many of present days healthcare applications. However, the essence of medical image diagnosis primarily depends on factors such as noise, features and classification accuracy in order to develop efficient methodology towards image segmentation, Image registration and most importantly towards automation of lower level and midlevel image processing. In this paper, a novel system is developed considering Gaussian, speckle and impulse noise models for which an improved adaptive filtering method is proposed for denoising. The novel feature extraction is performed as a two stage process considering the textural and geometrical properties of the image. Finally a classification process using Bayesian regularization back propagation method is used from the procured feature-sets. Experimental results show an improved performance considering image quality assessment metrics and also an improvement in the mean square error is observed as compared to other conventional methods.
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Raj, V.K., Majumder, A. (2017). Bayesian Regularization-Based Classification for Proposed Textural and Geometrical Features in Brain MRI. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_34
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DOI: https://doi.org/10.1007/978-3-319-57261-1_34
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