Mammogram denoising by curvelet transform based on the information of neighbouring coefficients

We present here an experimental work on mammogram denoising by the mathematical tool called the curvelet transform. The infiltration of noise in mammogram during the X-ray screening is a common and inevitable phenomenon. And such noise is normally reduced by the curvelet transform based on conventional thresholding strategy called the hard thresholding (HT). Therefore, the motive of this experimentation is to suggest an alternate but efficient mechanism of mammogram denoising by the same transform but with different algorithms purely based on the information of the neighbouring coefficients. It is found that the curvelet transform applied with the precept of surrounding curvelet coefficients is visually and statistically better than the conventional approach based on HT.