Wavelet transformation for enhancing mammographic images

Abstract

Objective: Mammographic images are often prone to noises and consequently make the task of radiologist to come up with the precise diagnosis. Although there are several denoising techniques for the same is available, while denoising they often suffer from the problem of eliminating the micron level details in the noise influenced images. It’s a trade-off which prohibits efficient micro-classification of mammary tissues. Methods: In this study, we present a solution for the same by utilizing multi-level wavelet transformation to enable preservation of micron level details in the images. Results and Conclusion: The quality denoising without elimination of the features of the mammographic imagery data by MWTA will allow the medical practitioner to easily identify and consequently diagnose properly to cancer influenced patients.