The quality of computed-tomography (CT) images deteriorates when images are reconstructed from incomplete data. This work makes use of the knowledge inherent in the membership functions and the logical rules of a fuzzy inference system (FIS) to compensate for the missing data. It is shown that a fuzzy inference system can be used to improve the quality of reconstructed CT images, particularly when the images are reconstructed from incomplete data. It is proposed to reconstruct a coarser image for which the data is over-complete, and use the histograms of this image and that of the original finer image to generate the membership functions required in FIS. The two images are then fused, with the aid of logical rules based on the knowledge that the two images posses the same distinct attributes (pixel values). In order to avoid the difference in spatial resolution between the original fine image and the reconstructed coarse image, a modified FIS method is introduced to refine the fine image. Results are presented, showing visually and quantitatively that this FIS refinement process improves the quality of the original fine image.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082559 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2021.e06839 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Introducing an integrated approach for fire safety assessment in healthcare facilities by interval valued neutrosophic-AHP and Fuzzy Inference System.
Heliyon
January 2025
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Fire safety in healthcare facilities is extremely important due to limited evacuation capacity of occupants. Therefore, poor fire safety precautions lead to more fatalities and financial losses. This study introduces an effective fire risk management approach for healthcare buildings utilizing an interval valued neutrosophic-fuzzy framework.
View Article and Find Full Text PDFAI-based processing of future prepared foods: Progress and prospects.
Food Res Int
February 2025
State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China; Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, 214122 Wuxi, Jiangsu, China.
The prepared foods sector has grown rapidly in recent years, driven by the fast pace of modern living and increasing consumer demand for convenience. Prepared foods are taking an increasingly important role in the modern catering industry due to their ease of storage, transportation, and operation. However, their processing faces several challenges, including labor shortages, inefficient sorting, inadequate cleaning, unsafe cutting processes, and a lack of industry standards.
View Article and Find Full Text PDFAn exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.
Sci Rep
January 2025
Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Budh Nagar, Noida, 201313, India.
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM).
View Article and Find Full Text PDFA fuzzy logic-based risk evaluation and precaution level estimation of explosive, flammable, and toxic chemicals for preventing damages.
Heliyon
January 2025
Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh.
Chemical industries are highly vulnerable to accidental events or terrorist attacks due to their processing, storage, and transportation of explosive, flammable, and toxic materials. Major industrial risks include fire, explosion, and toxic chemical release. An effective risk evaluation system is essential to prevent accidents or terrorist attacks.
View Article and Find Full Text PDFAdaptive hybrid ANFIS-PSO and ANFIS-GA approach for occupational risk prediction.
Int J Occup Saf Ergon
January 2025
Computer Science Department; Badji Mokhtar University, Algeria.
This study attempted to optimize the adaptive neuro-fuzzy inference system (ANFIS) using particle swarm optimization (PSO) and a genetic algorithm (GA) for calculating occupational risk. Numerous studies have shown that the ANFIS is a good approach for predicting engineering problems. However, it is not well investigated in the area of risk assessment.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!