All systems operational
Q4 2020

Enhanced Mammography image for Breast cancer detection using LC-CLAHE technique

Shada Omer Khanbari · Adel Sallam M. Haider
10.47372/uajnas.2020.n1.a12 386 Views 3 Citations
3
Citations
386
Views
Abstract

Breast cancer is the greatest challenging health complexities that medical science is facing. Most cases can be prevented by early detection and diagnosis which are the best way to cure breast cancer to decrease the mortality rate. The aim of this research is to obtain a method for enhancing the mammography images by using the proposed method which is incorporating the Local Contrast with Contrast Limited Adaptive Histogram Equalization (LC-CLAHE) to improve the appearance and to increase the contrast of the image and then de-noised by 2D wiener filter techniques. To extract the region of interest (tumor), we used region growing technique for the segmentation process. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. Efficiency is measured by Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). It is observed that the proposed method with wiener filter gives higher (PSNR) and lower (RMSE), with a significant filter mask [3 3].

Cite this Article (APA)
Shada, O. K., Adel, S. M. H. (2020). Enhanced Mammography image for Breast cancer detection using LC-CLAHE technique. University of Aden Journal of Natural and Applied Sciences. https://doi.org/10.47372/uajnas.2020.n1.a12
Related Papers
Exact solutions of the Harry Dym Equation using Lie group method
Mobarek Awadh Assabaai; Omer Faraj Mukherij · 2020
5
cites
389
Synthesis and characterization of copper oxide nanoparticles using Moringa Oleifera leaves Extract
Mahmood Mohammed Ali Saleh; Tawfik Mahmood Mohammed Ali; Rafig Mohamed Qassim Al · 2023
3
cites
389
2
cites
386
Access
View Full Text via DOI
Published in
ISSN 1606-8947
Quartile Q4
AMS Score 37
Field Natural Sciences
Publisher University of Aden
Country 🇾🇪 Yemen
View Journal Profile →
Authors
Publication Details
Year 2020
Language English
Added 06 Aug 2026