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Advancing mammography breast mass detection through diffusion segmentation

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Abstract

Medicine has become a necessary component of our daily life in the modern world. In this ever-changing environment, computer-aided diagnosis (CAD) has evolved as a dynamic and essential topic, providing crucial assistance to medical practitioners in their diagnostic endeavors. The main goal of this project is to create a CAD system that provides radiologists with a trustworthy second opinion during comprehensive detection of downscaled MIAS data. Our innovative approach is based on anisotropic denoising and the use of an alternating sequential filter (ASF) to identify potential mass sites. The method begins with ASF extraction through mathematical morphology, paving the way for overall contrast enhancement. To achieve this crucial aspect of our work, we use image-based active geometric contour models (level set) for spot segmentation. Our proposed strategy enables significant image improvements, leading to an increase in detection accuracy and efficient extraction of masses from the mini-MIAS mammography dataset. Experimental findings demonstrated that, when compared to contemporary methods, the proposed method produces satisfactory outcomes. The proposed technique achieved a sensitivity of 93.2%, an accuracy of 97.09%, a Dice coefficient (F1-score) exceeding 95.32%, and a specificity of 97.42%.

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Availability of data and materials

All the data and materials could be found at Centre for development of advanced technologies, Algiers, Algeria.

References

  1. Touil A, Kalti K, Conze PH, Solaiman B, Mahjoub MA (2020) Automatic detection of microcalcification based on morphological operations and structural similarity indices. Biocybernetics Biomed Eng 40(3):1155–1173

    Article  Google Scholar 

  2. Loizidou K, Elia R, Pitris C (2023) Computer-aided breast cancer detection and classification in mammography: a comprehensive review. Computers in Biology and Medicine, 106554

  3. Huffpost Maghreb (2015) http://www.huffpostmaghreb.com/2015/04/05/cancer-sein-algerie-n_7007174.html. Accessed 03 Dec 2015

  4. Guerroudji MA, Ameur Z (2015) New approaches for contrast enhancement of calcifications in mammography using morphological enhancement. In: Proceedings of the international conference on intelligent information processing, security and advanced communication, pp 1–5

  5. Diaz-Huerta CC, Felipe- Riveron EM, Montaño- Zetina LM (2014) Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms. Expert Syst Appl 41(16):7361–7369

    Article  Google Scholar 

  6. Nanayakkara RR, Yapa YPRD, Hevawithana PB, Wijekoon P (2015) Automatic breast boundary segmentation of mammograms. Int J Soft Comput Eng (IJSCE) 5(1):2231–2307

    Google Scholar 

  7. Guerroudji MA, Amara K, Aouam D, Zenati N, Djekoune O, Benbelkacem S (2022) Segmentation of the breast masses in mammograms using active contour for medical practice: AR based surgery. In: Lejdel B, Clementini E, Alarabi L (eds) Artificial intelligence and its applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham

  8. Olota M, Alsadoon A, Alsadoon OH, Dawoud A, Prasad PWC, Islam R, Jerew OD (2023) Modified anisotropic diffusion and level-set segmentation for breast cancer. Multimedia Tools and Applications, 1-23

  9. Atrey K, Singh BK, Roy A, Bodhey NK (2023) A dual-modality evaluation of computer-aided breast lesion segmentation in mammogram and ultrasound using customized transfer learning approach. Signal, Image and Video Processing, pp 1–9

  10. Kuttan GO, Elayidom MS (2023) Review on computer aided breast cancer detection and diagnosis using machine learning methods on mammogram image. Current Medical Imaging 19(12):1361–1371

    Google Scholar 

  11. Amara K, Kerdjidj O, Guerroudji MA, Zenati N, Djekoune O (2023) Augmented reality visualization and interaction for COVID-19 CT-Scan NN automated segmentation: a validation study. IEEE Sensors J 23(11):12114–12123. https://doi.org/10.1109/JSEN.2023.3265997

    Article  Google Scholar 

  12. Amara K, et al (2022) Augmented reality for COVID-19 aid diagnosis: CT-Scan segmentation based deep learning. 2022 7th international conference on image and signal processing and their applications (ISPA), Mostaganem, Algeria. pp 1-6. https://doi.org/10.1109/ISPA54004.2022.9786298.

  13. Amara K, Guerroudji MA, Kerdjidj O, Zenati N, Ramzan N (2023) HOLOTumour: 6DoF Phantom Head pose estimation based deep learning and brain tumour segmentation for AR visualisation and interaction. In: IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2023.3305596.

  14. Boudouh SS, Bouakkaz M (2023) New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques. Multimedia Tools and Applications, 1-22

  15. Sivanandan R (2023) Bayesian optimized novel CNN for improved diagnosis from ultrasound breast tumor images. Multimedia Tools and Applications, 1-19

  16. Ittannavar SS, Havaldar RH (2022) Segmentation of breast masses in mammogram image using multilevel multiobjective electromagnetism-like optimization algorithm. Bio-Med Research International 2022

  17. Tong Y et al (2021) Improved U-net MALF model for lesion segmentation in breast ultra-sound images. Biomed Sig Process Control 68:102721

    Article  Google Scholar 

  18. Vidal Joel, Vilanova Joan C, Marti Robert (2022) A U- Net ensemble for breast lesion segmentation in DCE MRI. Comput Biol Med 140:105093

    Article  Google Scholar 

  19. Guerroudji MA, Amara K, Benbelkacem S, Oulefki A, Zenati N, Aouam D, Masmoudi M (2021) Automatic brain tumor segmentation, and 3D reconstruction and visualization using augmented reality. In: 2021 International conference on artificial intelligence for cyber security systems and privacy (AI-CSP). IEEE, pp 1–5

  20. Liu C-C, Tsai C-Y, Liu J, Chun-Yuan Y, Yub S-S (2012) A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis. Comput Math Appl 64(1):1100–1107

    Article  Google Scholar 

  21. Patil RS, Biradar N, Pawar R (2022) A new automated segmentation and classification of mammogram images. Multimed Tools Appl 81(6):7783–7816

    Article  Google Scholar 

  22. mini MIAS (2014) The mini-MIAS database of mammograms. http://peipa.essex.ac.uk/info/mias.htm. Accessed 05 June 2015

  23. Lekamlage CD, Afzal F, Westerberg E, Cheddad A (2020) Mini-DDSM: mammography-based automatic age estimation. In: 2020 3rd International conference on digital medicine and image processing. pp 1-6

  24. Perona P, Malik J (1990) Scale space edge detection using Anisotropic diffusion. IEEE Trans Pattern Anal 12:629–639

    Article  Google Scholar 

  25. HADJIDJ I (2011) Analyse des Images Mammographiques pour lAide la Détection du Cancer du Sein. Magister memory in biomedical electronics, Abou Bekr Belkaid University, Tlemcen Algeria

  26. Caselles V, Catté F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66(1):1–31

    Article  MathSciNet  Google Scholar 

  27. Malladi R, Sethian J, Vemuri B (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175

    Article  Google Scholar 

  28. Fan T, Wang G, Li Y, Wang Z, Wang H (2021) A multi-scale information fusion level set for breast tumor segmentation. J Med Imaging Health Inf 11(8):2124–2132

    Article  Google Scholar 

  29. GR BR, Kumar HP (2023) Segmentation of mammogram images using level set with Cuckoo search optimisation. Comput Methods Biomech Biomed Eng: Imaging Vis 11(3):914–921

    Google Scholar 

  30. Zeiser FA, da Costa CA, Zonta T, Marques NM, Roehe AV, Moreno M, da Rosa Righi R (2020) Segmentation of masses on mammograms using data augmentation and deep learning. J Digit Imaging 33:858–868

    Article  Google Scholar 

  31. Begum AS, Kalaiselvi T, Rahimunnisa K (2022) A computer aided breast cancer detection using unit-linking pulse coupled neural network multiphase level set method. J Biomat Tissue Eng 12(8):1497–1504

    Article  Google Scholar 

  32. Yuvaraj K, Ragupathy US (2022) Hybrid active contour mammographic mass segmentation and classification. Comput Syst Sci Eng 40(3):823–834

    Article  Google Scholar 

  33. Radhi EA, Kamil MY (2021) Breast tumor detection via active contour technique. Int J Intell Eng Syst 14(4)

  34. Shen X, Ma H, Liu R, Li H, He J, Wu X (2021) Lesion segmentation in breast ultrasound images using the optimized marked watershed method. Biomed Eng Online 20(1):57

  35. Ittannavar SS, Havaldar RH (2022) Segmentation of breast masses in mammogram image using multilevel multiobjective electromagnetism-like optimization algorithm. BioMed Res Int 2022

  36. Hassan SA, Sayed MS, Farag F (2014) Segmentation of breast cancer lesion in digitized mammogram images. In: 2014 Cairo international biomedical engineering conference (CIBEC). IEEE, pp 103–106

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Authors and Affiliations

  1. CDTA Centre for Development of Advanced Technologies, Algiers, Algeria

    Mohamed Amine Guerroudji, Kahina Amara & Nadia Zenati

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  1. Mohamed Amine Guerroudji
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  2. Kahina Amara
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  3. Nadia Zenati
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Correspondence to Mohamed Amine Guerroudji.

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Guerroudji, M.A., Amara, K. & Zenati, N. Advancing mammography breast mass detection through diffusion segmentation. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18840-6

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