(46-2) 15 * << * >> * Russian * English * Content * All Issues
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
1 Department of Radiology, Faculty of Medicine Universitas Sebelas Maret,
Dr. Moewardi General Hospital, Surakarta, Indonesia. 57126,
2 Department of Physics - College of Science - Salahaddin University-Erbil – Iraq,
3 University of Applied Science and Technology, Center of Biarjomand Municipality, Iran,
4 Information and Communication Technology Research Group,
Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq,
5 Sechenov First Moscow State Medical University,Moscow, Russia,
6 Faculty of Biology and Ecology, Yanka Kupala State University of Grodno, 230023 Grodno, Belarus,
7 Department of Pathological Analysis Techniques/Al-Nisour University College / Iraq,
8 Optical Department, College of Health and Medical Technology, Sawa University,
Ministry of Higher Education and Scientific Research,Al-Muthanaa, Samawah, Iraq
PDF, 839 kB
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.
- Saxena S, Gyanchandani M. Machine learning methods for computer-aided breast cancer diagnosis using histopathology: A narrative review. J Med Imaging Radiat Sci 2020; 51(1): 182-193.
- Joshua LM, Huda F, Rao S, Ravi B. Clinicopathological significance of immunohistochemical expression of Filamin A in breast cancer. J Carcinog 2020; 19: 13. DOI: 10.4103/jcar.JCar_9_20.
- Feng H, Cao J, Wang H, Xie Y, Chen B. A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI. Magn Reson Imaging 2020; 69: 40-48.
- Bayraktar S, Batoo S, Okuno S, Glück S. Immunotherapy inbreast cancer. J Carcinog 2019; 18: 2. DOI: 10.4103/jcar.JCar_2_19.
- Devakumari, D., and Punithavathi, V. Comparison of noise removal filters for breast cancerdetection in mammogram images. Int J Pure Appl Math 2018; 119(18): 3863-3874.
- Batoo S, Bayraktar S, Al-Hattab E, Basu S, Okuno S, Glück S. Recent advances and optimal management of human epidermal growth factor receptor-2-positive early-stage breast cancer. J carcinog 2019; 18: 5. DOI: 10.4103/jcar.JCar_14_19.
- Wajid SK, Hussain A. Local energy-based shape histogram feature extraction technique for breast cancer diagnosis. Expert Syst Appl 2015; 42(20): 6990-6999.
- Sangeetha R, Murthy KS. A novel approach for detection of breast cancer at an early stage using digital image processing techniques. Int Conf on Inventive Systems and Control (ICISC) 2017: 1-4.
- Chowdhary CL, Acharjya DP. Breast cancer detection using intuitionistic fuzzy histogram hyperbolization and possibilitic fuzzy c-mean clustering algorithms with texture feature based classification on mammography images. Proc Int Conf on Advances in Information Communication Technology and Computing 2016: 21.
- Kannan S, Subiramaniyam NP, Rajamanickam AT, Balamurugan A. Performance comparison of noise reduction inmammogram images. Int J Res Eng Technol 2016; 5(2): 31-33.
- Jiang X, Wang Z, Zhang L, Stampanoni M. Noise-analysis-based non-local means method for X-ray grating-based mammography denoising. IEEE Trans Nucl Sci 2013; 60(2): 802-809.
- Talha M, Sulong G, Jaffar A. Preprocessing digital breast mammograms using adaptive weighted frost filter. Biomedical Research 2016; 27(4): 1407-1412.
- Elyasi I, Pourmina MA, Moin M-S. Speckle reduction in breast cancer ultrasound images by using homogeneity modified Bayes shrink. Measurement 2016; 91; 55-65.
- Eckert D, Vesal S, Ritschl L, Kappler S, Maier A. Deep learning-based denoising of mammographic images using physics-driven data augmentation. In Book: Tolxdorff T, Deserno T, Handels H, Maier A, Maier-Hein K, Palm C, eds. Bildverarbeitung für die Medizin 2020. Informatik aktuell. Wiesbaden: Springer Vieweg; 2020. DOI: 10.1007/978-3-658-29267-6_21.
- Abbas Q, Celebi ME, Garcia IF. Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system. Biomed 2013; 8: 204-214.
- Pereira DC, Ramos RP, Do Nascimento MZ. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Progr Biomed 2014; 114: 88-101.
- Cordeiro FR, Santos WP, Silva-Filho AG. An adaptive semi-supervised Fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images. Appl Soft Comput 2016; 46: 613-628.
- El Adoui M, Mahmoudi SA, Larhmam AM, Benjelloun M. MRI breast tumor segmentation using different encoder and decoder CNN architectures. MDPI Computers 2019; 8(3): 52.
- Dalmıs MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern-Merida A. Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med Phys 2017; 44: 533-546.
- Moeskops P, Wolterink JM, van der Velden BH, Gilhuijs KG, Leiner T, Viergever MA, Isgum I. Deep learning for multitask medical image segmentation in multiple modalities. Int Conf on Medical Image Computing and Computer-Assisted Intervention 2016: 478-486.
- Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proc 2016 Fourth Int Conf on 3D Vision 2016: 565-571.
- Mouelhi A, Rmili H, Ali BJ, Sayadi M, Doghri R, Mrad K. Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images. Comput Methods Programs Biomed 2018; 165: 37-51.
- Punitha S, Amuthan A, Joseph KS. Benign and malignant breast cancer segmentation using optimized region growing technique. Future Comput Inf J 2018; 3(2): 348-358.
- Rouhi R, Jafari M, Kasaei S, Keshavarzian P. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 2015; 42(3): 990-1002.
- Khalilabad ND, Hassanpour H. Employing image processing techniques for cancer detection using microarray images. Comput Biol Med 2016; 81(1): 139-147.
- Kaymak S, Helwan A, Uzun D. Breast cancer image classification using artificial neural networks. Procedia Comput Sci 2017; 120: 126-131.
- Karabatak M. A new classifier for breast cancer detection based on Naïve Bayesian. Measurement 2015; 72: 32-36.
- Wang F, Zhang S, Henderson LM. Adaptive decision-making of breast cancer mammography screening: A heuristic-based regression model. Omega 2018; 76: 70-84.
- Mohebian MR, Marateb HR, Mansourian M, Mañanas MA, Mokarian F. A hybrid computer-aided-diagnosis system for prediction of Breast Cancer Recurrence (HPBCR) using optimized ensemble learning. Comput Struct Biotechnol J 2017; 15: 75-85.
© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: firstname.lastname@example.org ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20