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Noise reduction and mammography image segmentation optimization with novel QIMFT-SSA method
W. Soewondo 1, S.O. Haji 2, M. Eftekharian 3, H.A. Marhoon 4, A.E. Dorofeev 5, A.T. Jalil 6, M.A. Jawad 7, A.H. Jabbar 8

Department of Radiology, Faculty of Medicine Universitas Sebelas Maret,
Dr. Moewardi General Hospital, Surakarta, Indonesia. 57126,
Department of Physics - College of Science - Salahaddin University-Erbil – Iraq,
University of Applied Science and Technology, Center of Biarjomand Municipality, Iran,
Information and Communication Technology Research Group,
Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq,
Sechenov First Moscow State Medical University,Moscow, Russia,
Faculty of Biology and Ecology, Yanka Kupala State University of Grodno, 230023 Grodno, Belarus,
Department of Pathological Analysis Techniques/Al-Nisour University College / Iraq,
Optical Department, College of Health and Medical Technology, Sawa University,
Ministry of Higher Education and Scientific Research,Al-Muthanaa, Samawah, Iraq

 PDF, 839 kB

DOI: 10.18287/2412-6179-CO-808

Pages: 298-307.

Full text of article: English language.

Breast cancer is one of the most dreaded diseases that affects women worldwide and has led to many deaths. Early detection of breast masses prolongs life expectancy in women and hence the development of an automated system for breast masses supports radiologists for accurate diagnosis. In fact, providing an optimal approach with the highest speed and more accuracy is an approach provided by computer-aided design techniques to determine the exact area of breast tumors to use a decision support management system as an assistant to physicians. This study proposes an optimal approach to noise reduction in mammographic images and to identify salt and pepper, Gaussian, Poisson and impact noises to determine the exact mass detection operation after these noise reduction. It therefore offers a method for noise reduction operations called Quantum Inverse MFT Filtering and a method for precision mass segmentation called the Optimal Social Spider Algorithm (SSA) in mammographic images. The hybrid approach called QIMFT-SSA is evaluated in terms of criteria compared to previous methods such as peak Signal-to-Noise Ratio (PSNR) and Mean-Squared Error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison to state-of-arts methods. supported the work.

breast cancer, noise reduction, image segmentation, mammography, QIMFT-SSA.

Soewondo W, Haji SO, Eftekharian M, Marhoon HA, Dorofeev AE, Jalil AT, Jawad MA, Jabbar AH. Noise reduction and mammography image segmentation optimization with novel QIMFT-SSA method. Computer Optics 2022; 46(2): 298-307. DOI: 10.18287/2412-6179-CO-808.


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