Aida FOOLADIVANDA, Nasrin AHMADINEJAD, Shahriar BARADARAN SHOKOUHI

Breast-region segmentation in MRI using chest region atlas and SVM

An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of broglandular tissue and the chest wall, the segmentation process is difficult for breasts with broglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method rst detects the approximated breast{chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of broglandular tissue to the chest wall by the extracted features from the obtained breast{chest wall boundary. Finally, the obtained breast{chest wall boundary is accurately re ned using the geometric shape of the chest region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of 5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN), and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.

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[1] American Cancer Society. Breast Cancer Facts and Figures 2013{2014. Atlanta, GA, USA: American Cancer Society, 2014.
[2] Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, Morris E, Pisano E, Schnall M, Sener S et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA-Cancer J Clin 2007; 57: 75-89.
[3] Behrens S, Laue H, Althaus M, Bohler T, Kuemmerlen B, Hahn HK, Peitgen HO. Computer assistance for MR based diagnosis of breast cancer: present and future challenges. Comput Med Imag Graph 2007; 31: 236-247.
[4] Wei J, Chan HP, Helvie MA, Roubidoux MA, Sahiner B, Hadjiiski LM, Zhou C, Paquerault S, Chenevert T, Goodsitt MM. Correlation between mammographic density and volumetric broglandular tissue estimated on breast MR images. Med Phys 2004; 31: 933-942.
[5] Nie K, Chen JH, Chan S, Chau MK, Yu HJ, Bahri S, Tseng T, Nalcioglu O, Su MY. Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med Phys 2008; 35: 5253-5262.
[6] Morris EA, Liberman L. Breast MRI: Diagnosis and Intervention. New York, NY, USA: Springer-Verlag, 2005.
[7] Ertas G, Gulcur HO, Osman O, Ucan ON, Tunaci M, Dursun M. Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching. Comput Biol Med 2008; 38: 116-126.
[8] Ertas G, Doran SJ, Leach MO. A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization. Med Biol Eng Comput 2017; 55: 57-68.
[9] Hayton P, Brady M, Tarassenko L, Moore N. Analysis of dynamic MR breast images using a model of contrast enhancement. Med Image Anal 1997; 1: 207-224.
[10] Yao J, Chen J, Chow C. Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform. IEEE J Sel Top Signal Process 2009; 3: 94-100.
[11] Twellmann T, Lichte O, Nattkemper TW. An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE T Med Imaging 2005; 24: 1256-1266.
[12] Chen W, Giger ML, Lan L, Bick U. Computerized interpretation of breast MRI: investigation of enhancement- variance dynamics. Med Phys 2004; 31: 1076-1082.
[13] Shokouhi SB, Fooladivanda A, Ahmadinejad N. Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness lter. EURASIP J Adv Signal Processing 2017; 39: 1-11.
[14] Giannini V, Vignati A, Morra L, Persano D, Brizzi D, Carbonaro L, Bert A, Sardanelli F, Regge D. A fully automatic algorithm for segmentation of the breasts in DCE-MR images. In: Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 31 August{4 September 2010; Buenos Aires, Argentina. New York, NY, USA: IEEE. pp. 3146-3149.
[15] Wu SD, Weinstein SP, Conant EF, Schnall MD, Kontos D. Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images. Med Phys 2013; 40: 42301-42312.
[16] Lin M, Chen JH, Wang X, Chan S, Chen S, Su MY. Template-based automatic breast segmentation on MRI by excluding the chest region. Med Phys 2013; 40: 122301-122310.
[17] Wang L, Platel B, Ivanovskaya T, Harz M, Hahn HK. Fully automatic breast segmentation in 3D breast MRI. In: Proceedings of the IEEE International Symposium on Biomedical Imaging; 2012; New York, NY, USA: IEEE. pp. 1024-1027.
[18] Jiang L, Hu X, Xia Q, Gu Y, Li Q. Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images. Med Phys 2017; 44: 2400-2414.
[19] Rosado-Toro JA, Barr T, Galons JP, Marron MT, Stopeck A, Thomson C, Thompson P, Carroll D, Wolf E, Altbach MI et al. Automated breast segmentation of fat and water MR images using dynamic programming. Acad Radiol 2015; 22: 139-148.
[20] Milenkovic J, Chambers O, Music MM, Tasic JF. Automated breast-region segmentation in the axial breast MR images. Comput Biol Med 2015; 62: 55-64.
[21] Rohl ng T, Brandt R, Menzel R, Russakoff DB, Maurer CR. Quo Vadis, atlas-based segmentation? In: Suri J, Wilson DL, Laxminarayan S, editors. The Handbook of Medical Image Analysis, Vol. III: Registration Models. New York, NY, USA: Kluwer Academic/Plenum Publishers, 2005. pp. 435-486.
[22] Gubern-Merida A, Kallenberg M, Mann RM, Marti R, Karssemeijer N. Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE J Biomed Health Inform 2015; 19: 349-357.
[23] Gallego-Ortiz C, Martel AL. Automatic atlas-based segmentation of the breast in MRI for 3D breast volume computation. Med Phys 2012; 39: 5835-5848.
[24] Khalvati F, Gallego-Ortiz C, Balasingham S, Martel AL. Automated segmentation of breast in 3-D MR images using a robust atlas. IEEE T Med Imaging 2015; 34: 116-125.
[25] Fooladivanda A, Shokouhi SB, Ahmadinejad N. Localized-atlas-based segmentation of breast MRI in a decision- making framework. Australas Phys Eng S 2017; 40: 69-84.
[26] Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: Signi cantly improved efficiency and reliability. NeuroImage 2006; 31: 1116-1128.
[27] Li C, Gore JC, Davatzikos C. Multiplicative intrinsic component optimization (MICO) for MRI bias eld estimation and tissue segmentation. Magn Reson Imaging 2014; 32: 913-923.
[28] Sauvola J, Pietikainen M. Adaptive document image binarization. Pattern Recogn 2000; 33: 225-236.
[29] Haralick RM, Shapiro LG. Computer and Robot Vision. Boston, MA, USA: Addison-Wesley, 1992.
[30] Al Youse H, Udpa S. Recognition of Arabic characters. IEEE T Pattern Anal 1992; 14: 853-857.
[31] Soille P. Morphological Image Analysis: Principles and Applications. New York, NY, USA: Springer-Verlag, 1999.
[32] Chang DH, Chen JH, Lin M, Bahri S, Yu HJ, Mehta RS, Nie K, Hsiang DJB, Nalcioglu O, Su MY. Comparison of breast density measured on MR images acquired using fat-suppressed versus nonfat-suppressed sequences. Med Phys 2011; 38: 5961-5968.
[33] Abe S. Support Vector Machines for Pattern Classi cation. London, UK: Springer Verlag, 2005.
[34] Steyvers M. Multidimensional Scaling. Encyclopedia of Cognitive Science. New York, NY, USA: Wiley, 2006.
[35] Myronenko A, Song X. Intensity-based image registration by minimizing residual complexity. IEEE T Med Imaging 2010; 29: 1882-1891.
[36] Saki F, Tahmasbi A, Soltanian Zadeh H, Shokouhi SB. Fast opposite weight learning rules with application in breast cancer diagnosis. Comput Biol Med 2013; 43: 32-41.
[37] Verma B, McLeod P, Klevansky A. Classi cation of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Exp Syst Appl 2010; 37: 3344-3351.
[38] Canu S, Grandvalet Y, Guigue V, Rakotomamonjy A. SVM and Kernel Methods MATLAB Toolbox. Rouen, France: INSA de Rouen, 2005.