| A Wavelet - Fuzzy Combination Based Approach for Efficient Cancer Characterization in Breast Ultrasound Images |
| Paper ID : 1012-ICEEM2021 (R3) |
| Authors: |
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Mohammed Tarek GadAllah *1, Ghada M. El-Banby2, Samir Mohammed Badawy3, Hassan El-Banna Zidan4, Abd El-Naser Abd-Elgawad Mohamed5, Alaa Abd-Elfattah Hefnawy6 1Assistant Researcher , Computers and Systems Department, Electronics Research Institute (ERI), Egypt 2Associate Professor, Industrial Electronics and Control Engineering Department, Faculty of Electronic Engineering, Menoufia University. Menouf, Menofia, Egypt 3Professor Emeritus ( Lecturer ), Industrial Electronics and Control Engineering Department, Faculty of Electronic Engineering, Menoufia University. Menouf, Menofia, Egypt 4Researcher, Computers and Systems Department, Electronics Research Institute (ERI), Egypt 5Professor Emeritus, Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Menoufia University. Menouf, Menofia, Egypt 6Researcher Professor, Computers and Systems Department, Electronics Research Institute (ERI), Egypt |
| Abstract: |
| Breast ultrasound (BUS) images are commonly influenced by speckle noise as a result of the phenomenon. Most types of speckle noise are a multiplicative one. Speckle removing while preserving image’s edges and image’s contrast is a challenge. More researches have been done throw the literature trying to fix a development at this point. Here, a modest scheme based on combining wavelet denoising and intuitionistic fuzzy enhancement has been proposed. Also, double thresholding image segmentation followed by some morphological operations were applied after the proposed approach. The proposed approach has been applied with a sonogram image of a breast having a benign cancer. Performance evaluation of the work has been achieved utilizing four quantitative metrics: mean square of error (MSE), peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), and the quantitative metric of edges’ conservation: Pratt’s figure of merit (FOM). Also, the method has been applied by a BUS image with a malignant cancer and a gray scale phantom. The final quantitative as well as qualitative results confirmed that the introduced approach could achieve an efficient success into breast cancer characterization through BUS images’ investigation, helping in better clinical diagnosis and more explicit detection for breast cancers. |
| Keywords: |
| Breast Cancer Characterization, Breast Ultrasound (BUS) Imaging, Wavelet Based Image Denoising, Fuzzy Based Image Enhancement, Wavelet Based Image Fusion, Double Thresholding Image Segmentation. |
| Status : Paper Published |
