ArticleQuantitative imaging in medicine and surgery2019
Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm.
Article in Quantitative imaging in medicine and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Methodology for the correction of a CBCT volume from the skull to the natural head position.MethodsX · 2024Article
- Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm.Journal of medical signals and sensors · 2024Article
- A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.Diagnostics (Basel, Switzerland) · 2023Review
- Exploratory models comparing ethiodized oil-glue and gold fiducials for bladder radiotherapy image-guidance.Physics and imaging in radiation oncology · 2021Article
- Geometric Regularized Hopfield Neural Network for Medical Image Enhancement.International journal of biomedical imaging · 2021Article
- Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features.Medical physics · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundIn this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm. Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient.
methodsIn this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization.
resultsThe simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed.
conclusionsFinal results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.
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