A cellular automata-based learning method for classification

Wongthanavasu, Sartra; Ponkaew, Jetsada

doi:10.1016/j.eswa.2015.12.003

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…CA have been also effectively applied in image processing [479,509] and Pattern Recognition and classification tasks [490,661], also with applications in cancer imaging [421]. In image processing, p represents a pixel (d = 2) or a voxel (d = 3) of the digital image I.…”

Section: Shortest Path With Cellular Automatamentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

102

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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Section: Shortest Path With Cellular Automatamentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

102

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“…Due to non-symmetry of the system, different transition rules take place producing similar states and roughness oscillations for some cells/states. This subsection will present a general system that can involve the growth and replication of cells [1,16]. It is easy to find a cell that can replicate itself in one of its died neighbours (trivial selfreproduction).…”

Section: Granule Cellular Automata With Gl =mentioning

confidence: 99%

“…Natural computation is a discipline that builds a bridge between computer science and natural science. Natural computations deal with the methodologies (including genetic algorithms, neural networks, and cellular automata) that take the inspiration from nature for problem solving and use of computation with real-life problems [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Multi‐level thinking cellular automata using granular computing title

Hassan

2018

IET Intelligent Transport Systems

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“…A cellular automaton (CA) is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology, and microstructure modeling [16]. Cellular automata can simulate a variety of real-world systems, including biological and chemical ones [17,18]. In complex electronic systems, the SEE soft errors induced by heavy ions or protons can be propagated and coupled.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Experimental Study of Soft Error Propagation Based on Cellular Automaton

Wang

Xing

et al. 2016

Mathematical Problems in Engineering

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Aiming to estimate SEE soft error performance of complex electronic systems, a soft error propagation model based on cellular automaton is proposed and an estimation methodology based on circuit partitioning and error propagation is presented. Simulations indicate that different fault grade jamming and different coupling factors between cells are the main parameters influencing the vulnerability of the system. Accelerated radiation experiments have been developed to determine the main parameters for raw soft error vulnerability of the module and coupling factors. Results indicate that the proposed method is feasible.

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A cellular automata-based learning method for classification

Cited by 13 publications

References 23 publications

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Multi‐level thinking cellular automata using granular computing title

Modeling and Experimental Study of Soft Error Propagation Based on Cellular Automaton

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