Effective and Accurate Diagnosis Using Brain Image Fusion

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.