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Effective and Accurate Diagnosis Using Brain Image Fusion

Effective and Accurate Diagnosis Using Brain Image Fusion

Sivakumar Rajagopal, Babu Gopal
Copyright: © 2023 |Pages: 21
ISBN13: 9781668475447|ISBN10: 1668475448|EISBN13: 9781668475454
DOI: 10.4018/978-1-6684-7544-7.ch050
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MLA

Rajagopal, Sivakumar, and Babu Gopal. "Effective and Accurate Diagnosis Using Brain Image Fusion." Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, edited by Information Resources Management Association, IGI Global, 2023, pp. 1000-1020. https://doi.org/10.4018/978-1-6684-7544-7.ch050

APA

Rajagopal, S. & Gopal, B. (2023). Effective and Accurate Diagnosis Using Brain Image Fusion. In I. Management Association (Ed.), Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention (pp. 1000-1020). IGI Global. https://doi.org/10.4018/978-1-6684-7544-7.ch050

Chicago

Rajagopal, Sivakumar, and Babu Gopal. "Effective and Accurate Diagnosis Using Brain Image Fusion." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, edited by Information Resources Management Association, 1000-1020. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7544-7.ch050

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Abstract

Medical imaging techniques are routinely employed to create images of the human system for clinical purposes. Multi-modality medical imaging is a widely used technology for diagnosis, detection, and prediction of various tissue abnormalities. This chapter is focused on the development of an improved brain image processing technique for the removal of noise from a magnetic resonance image (MRI) for accurate image restoration. Feature selection and extraction of MRI brain images are processed using image fusion. The medical images suffer from motion blur and noise for which image denoising is developed through non-local means (NLM) filtering for smoothing and shrinkage rule for sharpening. The peak signal to noise ratio (PSNR) of improved curvelet based self-similarity NLM method is better than discrete wavelet transform with an NLM filter.

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