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JOURNALS // Computer Optics // Archive

Computer Optics, 2022 Volume 46, Issue 2, Pages 298–307 (Mi co1018)

This article is cited in 3 papers

IMAGE PROCESSING, PATTERN RECOGNITION

Noise reduction and mammography image segmentation optimization with novel QIMFT-SSA method

W. Soewondoa, S. O. Hajib, M. Eftekharianc, H. A. Marhoond, A. E. Dorofeeve, A. T. Jalilf, M. A. Jawadg, A. H. Jabbarh

a Dr. Moewardi General Hospital, Surakarta, Indonesia
b Department of Physics – College of Science – Salahaddin University-Erbil – Iraq
c University of Applied Science and Technology, Center of Biarjomand Municipality, Iran
d Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq
e I. M. Sechenov First Moscow State Medical University
f Faculty of Biology and Ecology, Yanka Kupala State University of Grodno
g Department of Pathological Analysis Techniques, Al-Nisour University College, Iraq
h Optical Department, College of Health and Medical Technology, Sawa University, Al-Muthanaa, Samawah, Iraq

Abstract: 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.

Keywords: breast cancer, noise reduction, image segmentation, mammography, QIMFT-SSA

Received: 13.09.2020
Accepted: 02.11.2021

Language: English

DOI: 10.18287/2412-6179-CO-808



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