ArticleJournal of medical imaging (Bellingham, Wash.)2019
Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture.Diagnostics (Basel, Switzerland) · 2023Article
- Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey.Journal of imaging · 2022Review
- Extraction of region of interest from brain MRI by converting images into neutrosophic domain using the modified S-function.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification.Journal of digital imaging · 2020Article
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Authors and funding
6 authors.
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Abstract
We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region's edges and original image's brightness. In order to evaluate the proposed approach's performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset. A comparative study between the proposed method and four state-of-the art preprocessing algorithm attests that the proposed approach could yield a competitive performance for magnetic resonance brain glioblastomas tumor preprocessing. In fact, the result of this step of image preprocessing is very crucial for the efficiency of the remaining brain image processing steps: i.e., segmentation, classification, and reconstruction.
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