ArticleJournal of biomedical physics & engineering2016
Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth.
Article in Journal of biomedical physics & engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A novel physics-based model for fast computation of blood flow in coronary arteries.Biomedical engineering online · 2023Article
- A Dual-Modality Imaging Method Based on Polarimetry and Second Harmonic Generation for Characterization and Evaluation of Skin Tissue Structures.International journal of molecular sciences · 2023Article
- SL-HarDNet: Skin lesion segmentation with HarDNet.Frontiers in bioengineering and biotechnology · 2022Article
- EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization.Biomimetics (Basel, Switzerland) · 2021Article
- Article
- Closed- and Open-loop Deep Brain Stimulation: Methods, Challenges, Current and Future Aspects.Journal of biomedical physics & engineering · 2018Article
- MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique.Journal of medical systems · 2018Article
- Induction of Neuroplasticity by Transcranial Direct Current Stimulation.Journal of biomedical physics & engineering · 2016Article
- Deep Brain Stimulation and Gene Expression Alterations in Parkinson's Disease.Journal of biomedical physics & engineering · 2016Article
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Authors and funding
2 authors.
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Abstract
backgroundRegarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging.
objectiveThis study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth.
methodsAmong medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer's disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation.
resultsBy using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively.
conclusionThe study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases.
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27672629PMC5022759What OpenQuestion holds
Registered trials
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