Abstract
Breast cancer causes the highest number of deaths among all types of cancers in women. Therefore, it is necessary to properly diagnose the breast cancer for treatment of the patients. At present, dynamic contrast-enhanced (DCE)-MRI is widely used biomedical imaging technique to diagnose the breast cancer. In this paper, a breast DCE-MRI segmentation method using modified hard-clustering with Fireworks Algorithm (FWA) is developed for lesion detection. The segmentation of DCE-MRI suffers from noise and intensity inhomogeneities present in the images. On the outset, MR images are denoised using anisotropic diffusion filter and intensity inhomogeneities are corrected using max filter-based method in the preprocessing step. After that, images are segmented using hard-clustering technique with FWA algorithm. Finally, the lesions are extracted from the segmented images in the postprocessing step. The results of the proposed segmentation method are compared with segmentation methods based on Particle Swarm Optimizer (PSO), and K-means algorithms. The experimental results demonstrate that the proposed method outperforms other methods in the segmentation of breast DCE-MRI.
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References
https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/. Accessed online: 5 Dec 2019
Ferlay J, Soerjomataram I, Ervik M et al (2013) GLOBOCAN 2012 v1.0, Cancer incidence and mortality worldwide: IARC CancerBase No. 11 [Internet]. International Agency for Research on Cancer, F. Lyon
Bray F, Ren JS, Masuyer E et al (2013) Estimates of global cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer 132(5):1133–45
Shetty MK (Ed) (2015) Breast cancer screening and diagnosis—a synopsis. Springer Science+Business Media, New York
Wu Q, Salganicoff M, Krishnan A, Fussell DS, Markey MK.: (2006) Interactive lesion segmentation on dynamic contrast enhanced breast MR using a Markov Model. In: Reinhardt JM, Pluim JPW (Eds) Medical imaging 2006: image processing, Proc. of SPIE, vol 6144, 61444M
Azmi R, Norozi N (2011) A New Markov random field segmentation method for breast lesion segmentation in MR images. J. Med. Sig. Sens. 1(3):156
Jayender J, Gombos E, Chikarmane S, Dabydeen D, Jolesz FA, Vosburgh KG (2013) Statistical learning algorithm for in situ and invasive breast carcinoma segmentation. Comput Med Imaging Graph 37:281–292
Ribes S, Didierlaurent D, Decoster N, Gonneau E, Risser L, Feillel V, Caselles O (2014) Automatic segmentation of breast MR images through a Markov random field statistical model. IEEE Trans Med Imaging 33(10):1986–1996 Oct
Chen W, Giger ML, Bick U (2006) A Fuzzy C-Means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol 13:63–72
Shokouhi SB, Fooladivanda A, Ahmadinejad N (2017) Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter. EURASIP J Adv Signal Process 39
Nie K, Chen JH, Yu HJ, Chu Y, Nalcioglu O, Su MY (2008) Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 15:1513–1525
Yao J, Chen J, Chow C (2009) Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform. IEEE J Sel Top Signal Process 3(1):94–100 Feb
Wang Y, Morrell G, Heibrun ME, Payne A, Parker DL (2013) 3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction. Acad Radiol 20:137–147
Gubern-Mérida A, Kallenberg M, Mann RM, Martí R, Karssemeijer N (2015) Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE J Biomed Health Inform. 19(1):349–357
Yassin NIR, Omran S, Houby EMF El, Allam H (2017) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2017.12.012
Wei CH, Li Y, Huangc PJ, Gwoa CY, Harmse SE (2012) Estimation of breast density: an adaptive moment preserving method for segmentation of fibroglandular tissue in breast magnetic resonance images. Euro J Radiol 81:e618–e624
McClymont D, Mehnert A, Trakic A, Kennedy D, Crozier S (2014) Fully automatic lesion segmentation in breast MRI using mean-shift and graph-cuts on a region adjacency graph. J Magn Reson Imaging 39:795–804
Wang TC, Huang YH, Huang CS, Chen JH, Huang GY, Chang YC, Chang RF (2014) Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis. Magn Reson Imaging 32:197–205
Sim KS, Chia FK, Nia ME, Tso CP, Chong AK, Abbas SF, Chong SS (2014) Breast cancer detection from MR images through an auto-probing discrete Fourier transform system. Comput Biol Med 49:46–59
Gubern-Mérida A, Martí R, Melendez J, Hauth JL, Mann RM, Karssemeijer N, Platel B (2014) Automated localization of breast cancer in DCE-MRI. Med. Image Anal. https://doi.org/10.1016/j.media.2014.12.001
Khalvati F, Ortiz CG, Balasingham S, Martel AL (2015) Automated segmentation of breast in 3D MR images using a robust atlas. IEEE Trans Med Imaging 34(1):116–125
Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Tan Y et al (eds) ICSI 2010, Part I, LNCS, vol 6145. Springer, Heidelberg, pp 355–364
Tan Y (2015) Fireworks algorithm. Springer GmbH
Lingle W, Erickson BJ, Zuley ML, Jarosz R, Bonaccio E, Filippini J, Gruszauskas N (2016) Radiology data from the cancer genome atlas breast invasive carcinoma [TCGA-BRCA] collection. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2016.AB2NAZRP
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057
Mohana J, Krishnavenib V, Guo Y (2014) A survey on the magnetic resonance image denoising methods. Biomed Signal Process Control 9:56–69
Balafar MA, Ramli AR, Mashohor S (2010) A new method for MR grayscale inhomogeneity correction. Artif Intell Rev 34:195–204
Si T, De A, Bhattacharjee AK (2016) MRI brain lesion segmentation using generalized opposition-based glowworm swarm optimization. Int J Wavelets Multiresolut Inf Process 14(5):1650041 (29 pages)
Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227 Apr
Si T, De A, Bhattacharjee AK (2014) Brain MRI segmentation for tumor detection using Grammatical Swarm based clustering algorithm. In: Proceedings of IEEE international conference on circuits, power and computing technologies (ICCPCT-2014), Nagercoil, India
Si T, De A, Bhattacharjee AK (2015) Grammatical swarm based segmentation methodology for lesion segmentation in brain MRI. Int J Comput Appl 121(4):1–8
Derrac J, Garcĺa S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18
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Si, T., Mukhopadhyay, A. (2021). Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Fireworks Algorithm. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_2
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DOI: https://doi.org/10.1007/978-981-33-4604-8_2
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