An Improved Image Denoising and Segmentation Approach for Detecting Tumor from 2-D MRI Brain Images

@article{Faisal2012AnII,
  title={An Improved Image Denoising and Segmentation Approach for Detecting Tumor from 2-D MRI Brain Images},
  author={Ahmed Faisal and Sajida Parveen and Sk. S. Badsha and Hasan Sarwar},
  journal={2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT)},
  year={2012},
  pages={452-457}
}
Image denoising and segmentation are the two most challenging fields in medical image processing particularly when it is application specific. The presence of noise not only degrades the visual quality but also immensely affects the accuracies of segmentation which is essential for medical diagnosis process. In this paper, we present an improved approach for denoising and segmentation of 2-D magnetic resonance brain images for detecting the tumor. We use fourth order partial differential… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • The method is tested on several real brain MRI images and it shows 100% success in detecting tumor automatically.
  • We have tested our method on several Brain MRI images and it gives good PSNR result in terms of noise removal and 100% accuracy in terms of detecting tumor automatically.

Citations

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Extraction and description of tumour region from the brain MRI image using segmentation techniques

  • 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)
  • 2016
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Segmentation and compression of 2D brain MRI images for efficient teleradiological applications

  • 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)
  • 2016

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