Reaction-Diffusion Algorithm for Vision Systems

Nomura, Atsushi; Ichikawa, Makoto; Sianipar, Rismon Hasiholan; Miike, Hidetoshi

doi:10.5772/4954

Cited by 3 publications

References 23 publications

(22 reference statements)

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Image restoration and contrast enhancement based on a nonlinear reaction-diffusion mathematical model and divide & conquer technique

Alaa¹,

Atounti²,

Zirhem³

2021

Math. Model. Comput.

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In this article, we present a new algorithm for digital image processing noised by mixed Gaussian-impulse noise. Our mathematical model is based on the divide-conquer technique coupled with a reaction-diffusion system. We first decompose our image into low and high-frequency components by convolving each with a predefined convolutional filter. Further, we use a simple scheme of different weights to integrate and collect these processed sub-images into a filtered image. Finally, we apply our Reaction-Diffusion system to increase the contrast in the image. A number of experimental results are described to illustrate the performance of our algorithm and show that it is very effective in eliminating mixed Gaussian-impulse noise, increasing the contrast of the image and preserving the edges.

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Image restoration and contrast enhancement based on a nonlinear reaction-diffusion mathematical model and divide & conquer technique

Alaa¹,

Atounti²,

Zirhem³

2021

Math. Model. Comput.

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A Reaction-Diffusion Algorithm for Segmentation of Three-Dimensional Sinusoidal Networks in Rats Fed a High-Fat and High-Cholesterol Diet: New Insights and Evaluation

Shoji¹

2018

JBM

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Microstructures in the liver are primarily composed of hepatocytes, hepatic blood, and biliary vessels. Because each hepatocyte comes in contact with both vessels, these vessels form three-dimensional (3D) periodic network patterns. Confocal microscope images are useful for observing 3D structures; however, it is necessary to explicitly describe the vessel structures using 3D images of sinusoidal endothelial cells. For this purpose, we propose a new approach for image segmentation based on the Turing reaction-diffusion model, in which temporal and spatial patterns are self-organized. Turing conditions provided reliable tools for describing the 3D structures. Moreover, using the proposed method, the sinusoidal patterns of rats fed a high-fat/high-cholesterol diet were examined; these rats exhibited pathological features similar to those of human patients with nonalcoholic steatohepatitis related to metabolic syndrome. The findings showed that the parameter in diffusion terms differed significantly among the experimental groups. This observation provided a heuristic argument for parameter selection leading to pattern recognition problems in diseased rats.

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