Abstract
Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.
Highlights
In medical imaging, a cross-sectional sequence of high-resolution organs or tissues is obtained using CT, MRI, or other methods (Leng et al, 2013)
We propose a new method based on a recurrent type-2 fuzzy neural networks (RT2FNNs)
For the first time, these networks have been used for interpolation in medical images, and this is another innovation of this paper
Summary
A cross-sectional sequence of high-resolution organs or tissues is obtained using CT, MRI, or other methods (Leng et al, 2013). The direct use of such data for threedimensional (3D) image reconstruction often results in inaccurate images due to the heterogeneous dimensions of the images, the structure of discontinuous errors, sharp points, and other errors. To obtain volumetric (3D) data with isotropic dimensions and to reconstruct the 3D structure, it is essential to conduct several interpolations between the sections (Pan et al, 2012). One of the major problems is the presence of blind or undefined dots in one or more of the images. To address this issue a 2D interpolation operation is used (Hung et al, 2019). In (Leng et al, 2013), while expressing the problem of various categories of image
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