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| dc.contributor.author | Alam, Md. Maksud | |
| dc.contributor.author | Imtiaz, Shafiq | |
| dc.contributor.author | Hasan, Noor Kutub Al | |
| dc.contributor.author | Sarwar, Fahad Bin | |
| dc.date.accessioned | 2020-11-03T14:09:24Z | |
| dc.date.available | 2020-11-03T14:09:24Z | |
| dc.date.issued | 2018-11-15 | |
| dc.identifier.citation | [1] https://www.wcrf.org/dietandcancer/cancer-trends/breast-cancer-statistics [2] http://www.who.int/cancer/detection/breastcancer/en/index1.html [3] https://www.cancer.org/cancer/breast-cancer-in-men/about/key-statistics.html [4] M. P. Coleman, M. Quaresma, F. Berrino, J.-M. Lutz, R. De Angelis, R. Capocaccia, et al., "Cancer survival in five continents: a worldwide population-based study (CONCORD)," The lancet oncology, vol. 9, pp. 730-756, 2008. [5] Howlader N, Noone AM, Krapcho M, et al. SEER Cancer Statistics Review, 1975-2014. https://seer.cancer.gov/csr/1975_2014/, based on November 2016 SEER data submission, posted to the SEER web site, April 2017. Bethesda, MD: National Cancer Institute, 2017. [6] Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Incidence – SEER 18 Regs Research Data, Nov 2016 Sub (2000-2014) <Katrina/Rita Population Adjustment> – Linked To County Attributes – Total U.S., 1969-2015 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released April 2017, based on the November 2016 submission [7] B. Sahiner, H.-P. Chan, M. A. Roubidoux, L. M. Hadjiiski, M. A. Helvie, C. Paramagul, et al., "Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer- aided Diagnosis on Radiologist Accuracy 1," Radiology, vol. 242, pp. 716-724, 2007. [8] K. Drukker, M. L. Giger, K. Horsch, M. A. Kupinski, C. J. Vyborny, and E. B. Mendelson, "Computerized lesion detection on breast ultrasound," Medical physics, vol. 29, pp. 14381446, 2002. [9] https://www.breastcancer.org/symptoms/testing/types/mri/how_performed [10] Haas JS, Hill DA, Wellman RD, et al. Disparities in the use of screening magnetic resonance imaging of the breast in community practice by race, ethnicity, and socioeconomic status. Cancer. 2016;122: 611-617. [11] Huay-BenPan'The Role of Breast Ultrasound in Early Cancer Detection',Journal of Medical Ultrasound, Volume 24, Issue 4, December 2016, Pages 138-141 [12] Y. Cheung, Y. Wan, S. Chen, et al, Sonographic evaluation of mammographic detected microcalcification without a mass prior to stereotactic core needle biopsy J Clin Ultrasound, 30 (2003), pp. 323-331 [13] T. A. Krouskop, D. R. Dougherty, and F. S. Vinson, “A pulsed Doppler ultrasonic system for making noninvasive measurements of the mechanical properties of soft tissue,” J. Rehabil. Res.Dev., vol. 24, pp. 1–8, 1987. [14] R. M. Lerner and K. J. Parker, “Sono-elasticity in ultrasonic tissue characterization and echographic imaging,” inProc. 7th Eur. Comm. Workshop, J. M. Thijssen, Ed. Nijmegen, The Netherlands, 1987. [15] R. M. Lerner, S. R. Huang, and K. J. Parker, “‘Sonoelasticity’images derived from ultrasound signals in mechanically vibrated tissues,” Ultrason. Med. Biol., vol. 16, pp. 231–239, 1990. [16] Y. Yamakoshi, J. Sato, and T. Sato, “Ultrasonic imaging of internal vibration of soft tissue under forced vibration,” IEEE Trans. Ultrason., Ferroelect., Freq. Contr., vol. UFFC- 47, pp. 45–53, 1990. [17] J. Ophir, I. C´ espedes, H. Ponnekanti, Y. Yazdi, and X. Li, “Elastography: A method for imaging the elasticity in biological tissues,” Ultrason. Imaging, vol. 13, pp. 111–134, 1991. [18] M. O’Donnell, A. R. Skovoroda, B. M. Shapo, and S. Y. Emelianov, “Internal displacement and strain imaging using ultrasonic speckle tracking,” IEEE Trans. Ultrason., Ferroelect., Freq. Contr., vol. UFFC-41, pp. 314–325, 1994. [19] N. Duta and M. Sonka, “Segmentation and interpretation of MR brain images:Animprovedactiveshapemodel,” IEEETrans.Med.Imag.,vol. 17, pp. 1049–1062, Dec. 1998 [20] J. K. Udupa and S. Samarsekera, “Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation,” Graphical Models Image Processing, vol. 58, pp. 246–261, 1996. [21] N. R. Mudigonda, R. M. Ragayyan, and J. E. Leo Desautels, “Gradient and texture analysis for the classification of mammographic masses,” IEEE Trans. Med. Imag., vol. 19, pp. 1032–1043, Oct. 2000. [22] M. L. Giger et al., “Computerized analysis of lesions in ultrasound images of the breast,” Acad. Radiol., vol. 6, pp. 665–674, 1999. [23] S.Sudha, G.R.Suresh and R.Sukanesh, “Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance,” International Journal of Computer Theory and Engineering, Vol. 1, No. 1, April 2009 1793-8201 [24] D. Hillery, “Iterative wiener filters for Images restoration”. IEEE Transaction on SP, 39, pp. 1892-1899, 1991. [25] MICHAEL UNSER, SENIOR MEMBER, IEEE, AND AKRAM ALDROUBI, “A Review of Wavelets in Biomedical Applications ,“ PROCEEDINGS OF THE IEEE, VOL. 84, NO.4, APRIL 1996 [26] Ten Lectures on Wavelets. Philadelphia, PA: Soc. Ind. and AppL Math., 1992 [27] M. Vetterli and J. Kovacevic, Wavelets and Subband Coding. Englewood Cliffs, NJ: Prentice Hall, 1995. [28] D. L. Donoho and I. M. Johnstone, ” Ideal spatial adaptation via wavelet shrinkage” Biometrika, vol. 81, pp. 425–455, 1994. [29] Denver, Fodor I. K, Kamath. C, ” Denoising Through Wavelet Shrinkage,” An Empirical Study, Journal of Electronic Imaging. 12, pp.151-160, 2003. [30] D. L. Donoho and I. M. Johnstone, ”Adapting to unknown smoothness via wavelet shrinkage” Journal American Statistical. Association. vol. 90, no. 432, pp. 1200–1224, 1995 [31] https://www.sciencedirect.com/science/article/pii/S2211568413000302 [32] Catheline S. “Interférométrie-speckle ultrasonore : application à la mesure d’élasticité.” Thèse de doctorat. Université Paris VII, 1998. [33] S. Catheline, J. Thomas, F. Wu, M. Fink, “Diffraction field of a low frequency vibrator in soft tissues using transient elastography,” IEEE Trans Ultrason Ferroelectr Freq Control, 46 (4) (1999), pp. 1013-1019 [34] Gennisson JL. Le palpeur acoustique : “un nouvel outil d’investigations des tissus biologiques”. Thèse de l’université Pierre-et-Marie-Curie Paris-6, 2003. [35] J.L. Gennisson, S. Catheline, S. Chaffaï, M. Fink, “Transient elastography in anisotropic medium: application to the measurement of slow and fast shear waves velocities in muscles," J Acoust Soc Am, 114 (1) (2003), pp. 536-541 [36] S. Catheline, J.L. Gennisson, G. Delon, R. Sinkus, M. Fink, S. Abouelkaram, et al. “Measurement of viscoelastic properties of homogeneous soft solid using transient elastography: an inverse problem approach,” J Acoust Soc Am, 116 (6) (2004), pp. 3734-3741 [37] S. Catheline, J.L. Gennisson, M. Fink, Measurement of elastic non-linearity of soft solid with transient elastography,” J Acoust Soc Am, 114 (6) (2003), pp. 3087-3091 [38] J Ophir, S K Alam, B Garra, F Kallel, E Konofagou, T Krouskop and T Varghese , “Elastography: ultrasonic estimation and imaging of the elastic properties of tissues“. [39] Fung Y.C., “Biomechanical Properties of Living Tissues,” Springer-Verlag, New York, 1981, Chapter 7 [40] Anderson W.A.D., Pathology, C.V. Mosby, St. Louis, 1953. [41] Jonathan OPHIR , Faouzi KALLEL, Tomy VARGHESE , Elisa KONOFAGOU, S. Kaisar ALAM , Thomas KROUSKOP, Brian GARRA , Raffaella RIGHETTI, “IMAGERIE ACOUSTIQUE ET OPTIQUE DES MILIEUX BIOLOGIQUES OPTICAL AND ACOUSTICAL IMAGING OF BIOLOGICAL MEDIA”. [42] Mark A. Lubinski, Stanislav Y . Emelianov, and Matthew O’Donnell, “Speckle Tracking Methods for Ultrasonic Elasticity Imaging Using Short-Time Correlation,” IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, VOL. 46, NO. 1, JANUARY 1999 [43] J. W. Betz, “Comparison of the deskewed short-time correlatorand the maximum likelihood correlator,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-32, pp. 285-294, Apr. 1984 [44] M. A. Lubinski, S.Y. Emelianov, A. R. Skovoroda, and M. O’Donnell, “Imaging lateral displacements using soft tissue incompressibility,” Ultrason. Imaging,, vol. 17, pp. 69-70, 1995. [45] A. R. Skovoroda, M. A. Lubinski, S. Y . Emelianov, and hi. O’Donnell, “Nonlinear estimation of the lateral displacement using tissue incompressibility,” IEEE Trans. Ult [46] S. Kaisar Alam, Member, IEEE, Jonathan Ophir, Member, IEEE, and Elisa E. Konofagou, “An Adaptive Strain Estimator for Elastography”, ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 45, no. 2, march 1998 [47] J. Ophir, I. Cespedes, H. Ponnekanti, Y. Yazdi, and X. Li, “Elastography: A method for imaging the elasticity in biological tissues",Ultrason. Imaging, vol. 13, pp. 111-134, 1991. [48] I. Cespedes, “Elastography: imaging of biological tissue elasticity,"Ph.D. dissertation, Univ. Houston, Houston, TX, 1993. [49] S. K. Alam and J. Ophir, “Reduction of signal decorrelation from mechanical compression of tissues by temporal stretching|applications to elastography," Ultrason. Med. Biol., vol. 23, no. 1, pp. 95{105, 1997. [50] Elisabeth Brusseau*, Jan Kybic, Jean-François Déprez, and Olivier Basset, “2-D Locally Regularized Tissue Strain Estimation from Radio-Frequency Ultrasound Images: Theoretical Developments and Results on Experimental Data “, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 27, NO. 2, FEBRUARY 2008 [51] S. L. Bridal, J.-M. Correas, A. Saied, and P. Laugier, “Milestones on the road to higher resolution, quantitative, and functional ultrasonic imaging,” Proc. IEEE, vol. 91, no. 10, pp. 1543–1561, Oct. 2003 [52] W. Leucht and D. Leucht, Teaching Atlas of Breast Ultrasound. New York: Thieme Medical, 2000, pp. 24–38 [53] Anant Madabhushi and Dimitris N. Metaxas,” Combining Low-, High-Level and Empirical Domain Knowledge for Automated Segmentation of Ultrasonic Breast Lesions.” IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 22, NO. 2, FEBRUARY 2003 [54] Liu B, Cheng HD, Huang J, Tian JW, Liu J, Tang XL. “Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance.” Ultrasound Med Biol 2009;35:1309–1324. [55] Joo S, Yang YS, Moon WK, Kim HC. “Computer-aided diagnosisof solid breast nodules: Use of an artificial neural network based on multiple sonographic features.” IEEE Trans Med Imaging 2004;23: 1292–1300. [56] Yap MH, Edirisinghe EA, Bez HE. “A novel algorithm for initial lesion detection in ultrasound breast images.” JAppl Clin Med Phys 2008;9: 2741. [57] JUAN SHAN, H. D. CHENG, and YUXUAN WANG, “Completely Automated segmentation approach for breast ultrasound images using multiple domain features .” Ultrasound in Med. & Biol., Vol. 38, No. 2, pp. 262–275, 2012 [58] Juan Shan, H. D. Cheng, Yuxuan Wang,” A novel automatic seed point selection algorithm for breast ultrasound images,” Dept. of Computer Science, Utah State University, Logan, UT 84322 [59] A. T. Stavros et al., “Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions,” Radiol., vol. 196, pp. 123–134, 1995. [60] I.Dydenko,D.Friboulet,J.M.Gorce,J.D’hooge,B.Bijnens,andI.E. Magnin,“Towardsultrasoundcardiacimagesegmentationbasedonthe radiofrequency signal,” Med. Image Anal., vol. 7, no. 3, pp. 353–367, 2003. [61] O. Husby and H. Rue, “Estimating blood vessel areas in ultrasound imagesusingadeformabletemplatemodel,”Statist.Model.,vol.4,no. 3, pp. 211–226, 2004. [62] P. H. Arger, C. Sehgal, E. Conant, J. Zuckerman, S. E. Rowling, and J. A. Paton, “Inter-reader variability and predictive value of US descriptions of solid breast masses,” Acad. Radiol., pp. 335–342, 2001. [63] W. Leucht and D. Leucht, “Teaching Atlas of Breast Ultrasound.” New York: Thieme Medical, 2000, pp. 24–38. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/644 | |
| dc.description | Supervised by Prof. Dr. Md. Ashraful Hoque Head, Department of Electrical and Electronic Engineering, Islamic University of Technology(IUT) Board Bazar, Gazipur-1704, Bangladesh. | en_US |
| dc.description.abstract | Breast cancer is the only cancer that is considered universal among women worldwide. Breast cancer is the most common cancer in American women, except for skin cancers. Currently, the average risk of a woman in the United States developing breast cancer sometime in her life is about 12%. Breast cancer is sometimes found after symptoms appear, but many women with breast cancer have no symptoms. This is why regular breast cancer screening is so important. There is no sure way to prevent breast cancer. Thus, early detection and treatment are crucial in minimizing breast cancer related deaths. Due to the inherent nature of ultrasound imaging such as uneven speckle patterns, no fixed threshold values, anisotropy and signal drop-out bio medical image processing is a challenging task. Automatic segmentation of BUS is very difficult due to uneven shape and imprecise boundary of breast lesions. In order to improve the problem prevalent in the existing methods, a complete qualitative analysis of Breast ultrasound (BUS) images for tumor detection is proposed in this thesis. The method involves four steps – (a) Speckle reduction using different filters, (b) Strain estimation by various methods, (c) 2D search for displacement and then application of 1.5D adaptive stretching and (d) Image segmentation for lesion detection. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Department of Electrical and Electronic Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh | en_US |
| dc.title | An Approach to Improve Breast Cancer Detection by Using Various Strain Estimation Methods and Image Segmentation on Breast Ultrasound (BUS) Images | en_US |
| dc.type | Thesis | en_US |
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