Kidsumran, Varakorn and Zheng, Yalin 
ORCID: 0000-0002-7873-0922
(2020)
Deep Vectorization Convolutional Neural Networks for Denoising in Mammogram Using Enhanced Image.
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Abstract
Mammography is an X-ray image of the breast which has been widely used for the management of breast cancer. However, in many cases, it is not easy to identify a sign of cancer as tumour or malignancy due to clouding various noise patterns caused by the low dose radiation from the X-ray machine. Mammogram denoising is an important process to improve the visual quality of mammogram to help the radiologist’s diagnosis when they screening mammogram. This paper introduces denoising deep vectorization convolutional neural networks using an enhanced image from direct contrast in a wavelet domain for training. Then, Denoised mammogram is obtained from mapping between the original and enhanced image. Mammogram image from the mini-MIAS database of mammograms was used in this experiment. The experimental results demonstrate that the proposed method can effectively suppress various noises in mammogram both qualitative and subjective test by comparison to traditional denoising methods.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Breast Cancer, Biomedical Imaging, Cancer, Cancer |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 08 Jun 2020 08:29 |
| Last Modified: | 17 Mar 2024 07:29 |
| DOI: | 10.1007/978-3-030-39343-4_19 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3089639 |
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