https://orcid.org/0000-0002-4372-1743
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ABSTRACT
Image enhancement techniques for digital mammograms can preserve the contrast to distinguish the diagnostic features such as masses and microcalcifications. However, mammogram images suffer low contrast and high image noise due to low exposure radiation used. As a result, the diagnostic features are difficult to detect and analyse by the radiologist. In this article, we propose a novel Fuzzy Anisotropic Diffusion Histogram Equalisation Contrast Adaptive Limited (FADHECAL) enhancement technique to reduce the noise of the mammogram images while preserving contrast and brightness. The FADHECAL technique also applies Fuzzy Clipped Inference System (FCIS) which automatically select clip-limit during the enhancement process. The mammogram images were retrieved from Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) from the University of South Florida database. The results have shown that FADHECAL has the most superior results among other selected enhancement techniques with 6.502 ± 1.855 of AMBE, 0.934 ± 0.034 of SSIM, 15.742 ± 1.217 of MAE, 26.843 ± 2.541 of PSNR, 0.969 ± 0.021 of UIQI and 1.151 ± 0.147 of RMSE values. This FADHECAL can be used as an ideal platform of image enhancement for mammogram images to detect the breast cancer lesions with better noise reduction and preserving the image details.
Acknowledgements
Medical Imaging Research Group (MIRG), Faculty of Health Sciences, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Saifullah Harith Suradi
Saifullah Harith Suradi is a research assistant at the Universiti Sultan Zainal Abidin (UniSZA). He received his B.Eng. degree in Biomedical Engineering from the University of Malaya (UM) in 2019 and currently doing his MSc in Medical Imaging at UniSZA. He focuses on developing techniques for medical image processing on towards improving clinical outcomes. His current research interests include medical image analysis and processing and artificial intelligence.
Kamarul Amin Abdullah
Kamarul Amin Abdullah received the BSc and MSc degrees in Medical Imaging from Universiti Teknologi Mara (UiTM) in 2009 and 2014, respectively, and the Ph.D. degree in Medical Radiation Sciences, in 2018 from the University of Sydney, Australia. He is currently a Senior Lecturer and the Deputy Dean (Academic and Graduates) with the Faculty of Health Sciences, UniSZA. His research interests include 3D printing, Computed Tomography (CT), digital image processing and medical imaging.
Nor Ashidi Mat Isa
Nor Ashidi Mat Isa received the B.Eng. degree (Hons.) in Electrical and Electronic Engineering from Universiti Sains Malaysia (USM), in 1999, and the Ph.D. degree in Electronic Engineering (majoring in image processing and artificial neural network), in 2003. He is currently a Professor and the Deputy Dean (Academic, Career, and International) with the School of Electrical and Electronic Engineering, USM. His research interests include intelligent systems, image processing, neural network, biomedical engineering, and intelligent diagnostic systems and algorithms.