Abstract
Breast cancer is a significant public health concern with over 2.2 million cases reported in 2022 by the World Health Organization. Computer-aided detection (CAD) systems that use machine learning (ML) have become a crucial tool in assisting clinicians with breast cancer diagnostics. Our proposal offers an augmented reality (AR) system that allows surgeons to visualize cancer tumors in a cost-effective manner. The approach involves noise suppression using a Median filter, Top-Hat transform for blocking clear spots, and segmentation of active geometric contour models based on speckle (plane set) image. The pro-posed approach was validated experimentally, with a sensitivity of 90% accuracy of over 98% for tumor cells. Using 3D Slicer, the 3D breast mass reconstruction can be virtually augmented on the actual scene, which significantly improves breast mass extraction, additional 3D reconstruction, 3D interaction, and AR visualization. Overall, the proposal presents a promising approach for assisting clinicians with breast cancer diagnosis and treatment.
Similar content being viewed by others
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
References
Perez-Ponce H (2009) Corrélation entre les performances physiques mesurées des détecteurs et la qualité diagnostique de l’image en mammographie numérique. Diss, Institut National Polytechnique de Lorraine-INPL
Herredsvela J, Gulsrud T, Engan K (2005) Detection of circumscribed masses in mammograms using morphological segmentation. Proceedings of SPIE - the international society for optical engineering
Duarte M, Alvarenga A, Azevedo C, Calas M, Infantosi A, Pereira W (2013) Segmenting mammographic microcalcifications using a semiautomatic procedure based on Otsu’s meth-od morphological filters. Braz J Biomed Eng 29:377–388
Liu C, Tsai C, Liu J, Yu C, Yub S (2012) A pectoral muscle segmentation algorithm for digi-tal mammograms using Otsu thresholding and multiple regression analysis. Comput Math Appl 1:1100–1107
Arikidis N, Karajaliou A, Skiadopoulos S, Korfiatis P, Likaki E, Panayiotakis G (2010) Size-adapted micro calcification segmentation in mammography utilizing scale-space signatures. Compute Med Imaging Graph 34:487–493
Soulami KB et al (2021) Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation. Biomed Signal Process Control 66:102481
Mishra S et al (2021) Breast cancer detection and classification using improved FLICM segmentation and modified SCA based LLWNN model. Computational vision and Bio-inspired computing. Springer, Singapore, pp 401–413
Biswas S, Hazra R (2021) A level set model by regularizing local fitting energy and penalty energy term for image segmentation. Signal Processing 183:108043
Gupta KK, Pahadiya P, Saxena S (2022) Detection of cancer in breast thermograms using mathematical threshold based segmentation and morphology technique. Int J Syst Assurance Eng Manag 13(1):421–428
Houssein EH, Emam MM, Ali AA (2021) An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on im-proved chimp optimization algorithm. Expert Syst Appl 185:115651
Naik MK, Panda R, Abraham A (2021) An entropy minimization based multilevel colour thresholding technique for analysis of breast thermograms using equilibri-um slime mould algorithm. Appl Soft Comput 113:107955
Tiwari A et al (2021) Evolutionary multi-level thresholding for breast thermogram segmen-tation. International conference on intelligent networking and collaborative systems, Springer, Cham
Snasel V (2021) Evolutionary multi-level thresholding for breast thermogram segmenta-tion. Advances in intelligent networking and collaborative systems: The 13th international conference on intelligent networking and collaborative systems (INCoS-2021). vol 312. Springer Nature
Thukral R, Arora AS, Kumar A, Gulshan (2022) Denoising of thermal images using deep neural network. In Proceedings of international conference on recent trends in computing: ICRTC 2021 (pp 827–833). Singapore: Springer Nature Singapore
Thukral R, Kumar A, Arora AS (2019) Effect of different thresholding techniques for denoising of emg signals by using different wavelets. In 2019 2nd International conference on intelligent communication and computational techniques (ICCT) pp 161–165. IEEE
Ittannavar SS, Havaldar RH (2022) Segmentation of breast masses in mammogram Im-age using multilevel multiobjective electromagnetism-like optimization algorithm. Bio-Med Research International 2022
Tong Y et al (2021) Improved U-net MALF model for lesion segmentation in breast ultra-sound images. Biomed Signal Process Control 68:102721
Salama WM, Aly MH (2021) Deep learning in mammography images seg-mentation and classification: Automated CNN approach. Alexandria Eng J 60(5):4701–4709
Yan Y et al (2022) Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images. Biomed Signal Process Control 72:103299
Vidal J, Vilanova JC, Martí R (2022) A U-Net Ensemble for breast lesion seg-mentation in DCE MRI. Comput Biol Med 140:105093
Su Y et al (2022) YOLO-LOGO: a transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms. Comput Methods Programs Biomed 106903
Kaur A et al (2022) Detection of breast cancer masses in mammogram images with watershed segmentation and machine learning approach. Artificial intelligence for Inno-vative healthcare informatics. Springer, Cham 35–60
Douglas DB et al (2017) Augmented reality: advances in diagnostic imaging. Multimodal Technol Interact 1(4):29
Gouveia PF et al (2021) Breast cancer surgery with augmented reality. The Breast 56:14–17
Risco P, Jing A (2022) AR application for sentinel lymph node detection in breast can-cer
Grácia S, Soudah E, de Cross O, Niñerola A (2021) Mixed reality system to study deformable objects: breast cancer application. Final Degree Project, Biomedical Engineer-ing Degree
La Padula S et al (2022) Assessment of patient satisfaction using a new augmented re-ality simulation software for breast augmentation: a prospective study. J Clini-cal Med 11(12):3464
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
Chauhan S, Singh M, Aggarwal AK (2023) Designing of optimal digital IIR filter in the multi-objective framework using an evolutionary algorithm. Eng Appl Artif Intell 119:105803
Chauhan S, Singh M, Aggarwal AK (2021) Design of a two-channel quadrature mirror filter bank through a diversity-driven multi-parent evolutionary algorithm. Circ Syst Signal Process 40:3374–3394
Chauhan S, Singh M, Agarwal AK (2019) Crisscross optimization algorithm for the designing of quadrature mirror filter bank. In 2019 2nd international conference on intelligent communication and computational techniques (ICCT) pp 124–130. IEEE
Bai X, Zhou F (2010) Infrared small target enhancement and detection based on modified top-hat transformations. Comput Electr Eng 36(6):1193–1201
Bai X, Zhou F (2010) Analysis of new top-hat transformation and the application for infrared dim small target detection. Pattern Recognit 43(6):2145–2156
Thirumala S, Chanamallu SR (2020) Tumor boundary delineation using abnormality outlining box guided modified GVF snake model. In Information, photonics and communication pp 135–144. Springer, Singapore
Gadi T, Benslimane R (2000) Fuzzy hierarchical segmentation, hierarchical fuzzy transmission segmentation. Laboratory and Image Processing Morocco
Zhou Y (2007) Cell segmentation using level set method, university Johannes Kepler. Angefertigt am Johann Radon Institute for Computational and Applied Mathematics
Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. Department of Electrical and Computer Engineering University of Connecticut Storrs USA
Foucault AD (2010) Numerical simulation of forest fires with reset and around obstacles, memory to obtain the degree of Master of Science University of Montreal
Guoqiang W, Dongxue W (2010) Segmentation of brain MRI image with GVF snake model. In 2010 First international conference on pervasive computing, signal processing and applications pp 711–714. IEEE
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Guerroudji, M.A., Amara, K. & Zenati, N. Augmented reality aid in diagnostic assistance for breast cancer detection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18979-2
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11042-024-18979-2