Breast mass segmentation using mammographic data: a systematic review

References

• Agarwal V, Carson C 2015 . Using deep convolutional neural networks to predict semantic features of lesions in mammograms. C231n Course Project Reports.
   Google Scholar
• Ait Lbachir I, Es-Salhi R, Daoudi I, Tallal S, Medromi H. 2017. A survey on segmentation techniques of mammogram images. In: International symposium on ubiquitous networking; advances in ubiquitous networking 2. Singapore: Springer; p. 545–556. doi:10.1007/978-981-10-1627-1_43.
   Google Scholar
• Bajger M, Ma F, Bottema MJ. 2005. Minimum spanning trees and active contours for identification of the pectoral muscle in screening mammograms. Digital Image Comput. 47. doi:10.1109/DICTA.2005.55.
   Google Scholar
• Bellotti R, De Carlo F, Tangaro S, Gargano G, Maggipinto G, Castellano M, Massafra R, Cascio D, Fauci F, Magro R, et al. 2006. A completely automated CAD system for mass detection in a large mammographic database. Med Phys. 33(8):3066–3075. doi:10.1118/1.2214177.
   PubMed Web of Science ®Google Scholar
• Bick U, Giger ML, Schmidt RA, Nishikawa RM, Wolverton DE, Doi K. 1995. Automated segmentation of digitized mammograms. Acad Radiol. 2(1):1–9. doi:10.1016/S1076-6332(05)80239-9.
   PubMed Web of Science ®Google Scholar
• Borges Sampaio W, Moraes Diniz E, Corrêa Silva A, Cardoso de Paiva A, Gattass M. 2011. Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Comput Biol Med. 41(8):653–664. doi:10.1016/j.compbiomed.2011.05.017.
   PubMed Web of Science ®Google Scholar
• Boulehmi H, Mahersia H, Hamrouni K 2016. Breast masses diagnosis using supervised approaches. In: 2016 International Image Processing, Applications and Systems (IPAS): IEEE; Hammamet, Tunisia. IEEE. p. 1–6. doi:10.1109/IPAS.2016.7880134.
   Google Scholar
• Bozek J, Mustra M, Delac K, Grgic M. 2009. A survey of image processing algorithms in digital mammography. In: Recent advances in multimedia signal processing and communications. Vol. 231 . Berlin, Heidelberg: Springer; p. 631–657. doi:10.1007/978-3-642-02900-4.
   Google Scholar
• Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 2018. Global cancer statistics 2018: gLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68(6):394–424. doi:10.3322/caac.21492.
   PubMed Web of Science ®Google Scholar
• Bruno A, Ardizzone E, Vitabile S, Midiri M. 2020. A novel solution based on scale invariant feature transform descriptors and deep learning for the detection of suspicious regions in mammogram images. J Med Signals Sens. 10(3):158. doi:10.4103/jmss.JMSS_31_19.
   PubMedGoogle Scholar
• Cahoon TC, Sutton MA, Bezdek JC 2000. Breast cancer detection using image processing techniques. Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063); San Antonio, TX, USA. IEEE. p. 973–976. doi: 10.1109/FUZZY.2000.839171.
   Google Scholar
• Cao Y, Hao X, Zhu X, Xia S. 2010. An adaptive region growing algorithm for breast masses in mammograms. Front Electr Electron Eng China. 5(2):128–136. doi:10.1007/s11460-010-0017-y.
   Google Scholar
• Cao Y, Xia S. 2009. An improved region-growing algorithm for mammographic mass segmentation. MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques. 7497:432–438.
   Google Scholar
• Cardoso JS, Marques N, Dhungel N, Carneiro G, Bradley AP. 2017. Mass segmentation in mammograms: a cross-sensor comparison of deep and tailored features. In: 2017 IEEE International Conference on Image Processing (ICIP); Beijing, China. IEEE. p. 1737–1741. doi: 10.1109/ICIP.2017.8296579.
   Google Scholar
• Cheng HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN. 2006. Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 39(4):646–668. doi:10.1016/j.patcog.2005.07.006.
   Web of Science ®Google Scholar
• Chouhan SS, Kaul A, Singh UP. 2019. Image Segmentation using computational intelligence techniques: review. Arch Computat Methods Eng. 26(3):533–596. doi:10.1007/s11831-018-9257-4.
   Web of Science ®Google Scholar
• Chu Y, Li L, Clark RA. 2002. Graph-based region growing for mass-segmentation in digital mammography. Medical Imaging 2002. Image Processing. 4684:1690–1697.
   Google Scholar
• Chu J, Min H, Liu L, Lu W. 2015. A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC super pixel segmentation. Med Phys. 42(7):3859–3869. doi:10.1118/1.4921612.
   PubMed Web of Science ®Google Scholar
• Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, et al. 2013. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 26(6):1045–1057. doi:10.1007/s10278-013-9622-7.
   PubMed Web of Science ®Google Scholar
• Cordeiro FR, Lima SML, Silva-Filho AG, dos Santos WP. 2012. Segmentation of mammography by applying extreme learning machine in tumor detection. IDEAL; p. 92–100. doi:10.1007/978-3-642-32639-4_12.
   Google Scholar
• Dabass J, Arora S, Vig R, Hanmandlu M. 2019. Segmentation techniques for breast cancer imaging modalities-a review. In: 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence); Noida, India. IEEE. p. 658–663. doi:10.1109/CONFLUENCE.2019.8776937.
   Google Scholar
• de Oliveira Martins L, Junior GB, Silva AC, de Paiva AC, Gattass M. 2009. Detection of masses in digital mammograms using K-means and support vector machine. ELCVIA Electronic Letters On Computer Vision And Image Analysis. 8(2):39–50. doi:10.5565/rev/elcvia.216.
   Google Scholar
• Dhungel N, Carneiro G, Bradley AP. 2015a. Deep Learning and structured prediction for the segmentation of mass in mammograms. In: Navab N, Hornegger J, Wells W, and Frangi A, editors. Medical image computing and computer-assisted intervention – MICCAI 2015. Springer International Publishing; p. 605–612. doi:10.1007/978-3-319-24553-9_74.
   Google Scholar
• Dhungel N, Carneiro G, Bradley AP. 2015b. Deep structured learning for mass segmentation from mammograms. In: 2015 IEEE International Conference on Image Processing (ICIP); Quebec City, QC, Canada. IEEE. p. 2950–2954. doi: 10.1109/ICIP.2015.7351343.
   Google Scholar
• Dhungel N, Carneiro G, Bradley AP. 2016. The automated learning of deep features for breast mass classification from mammograms. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, and Wells W, editors. Medical image computing and computer-assisted intervention – MICCAI 2016. Springer International Publishing; p. 106–114. doi:10.1007/978-3-319-46723-8_13.
   Google Scholar
• Din NM, Dar RA, Rasool M, Assad A. 2022. Breast cancer detection using deep learning: datasets, methods, and challenges ahead. Comput Biol Med. 149:106073. doi:10.1016/j.compbiomed.2022.106073.
   PubMed Web of Science ®Google Scholar
• Domínguez AR, Nandi AK. 2007. Improved dynamic‐programming‐based algorithms for segmentation of masses in mammograms. Med Phys. 34(11):4256–4269. doi:10.1118/1.2791034.
   PubMed Web of Science ®Google Scholar
• Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, Lian Z, Liu J, Luo X, Pei S, et al. 2018. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol. 28(2):582–591. doi:10.1007/s00330-017-5005-7.
   PubMed Web of Science ®Google Scholar
• El Atlas N, El Aroussi M, Wahbi M. 2014. Computer-aided breast cancer detection using mammograms: a review. In: 2014 Second World Conference on Complex Systems (WCCS); Agadir, Morocco. IEEE. p. 626–631. doi: 10.1109/ICoCS.2014.7060995.
   Google Scholar
• El Houby EMF. 2018. Framework of computer aided diagnosis systems for cancer classification based on medical images. J Med Syst. 42(8):157. doi:10.1007/s10916-018-1010-x.
   PubMed Web of Science ®Google Scholar
• ESENERİ I, Ergin S, Yüksel T. 2018. A novel multistage system for the detection and removal of pectoral muscles in mammograms. Turk J Elec Eng & Comp Sci. 26(1):35–49. doi:10.3906/elk-1703-272.
   Web of Science ®Google Scholar
• Fauci F, Bagnasco S, Bellotti R, Cascio D, Cheran SC, De CF, De Nunzio G, Fantacci ME, Forni G, Lauria A, et al. 2004. Mammogram segmentation by contour searching and massive lesion classification with neural network. IEEE Symposium Conference Record Nuclear Science 2004; Rome, Italy. IEEE. p. 2695–2699. doi:10.1109/NSSMIC.2004.1462823.
   Google Scholar
• Feng Q, Gao B, Lu P, Woo WL, Yang Y, Fan Y, Qiu X, Gu L. 2018. Automatic seeded region growing for thermography debonding detection of CFRP. Ndt&e Int. 99:36–49. doi:10.1016/j.ndteint.2018.06.001.
   Web of Science ®Google Scholar
• Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. 2020a. Estimated number of deaths in 2020, worldwide, women all ages. In: Global cancer observatory: cancer today. Lyon, France: International Agency for Research on Cancer. Accessed 20 March, 2021, Available from, https://gco.iarc.fr/today
   Google Scholar
• Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. 2020b. Estimated number of deaths in 2020, worldwide, women all ages. In: Global cancer observatory: cancer today. Lyon, France: International Agency for Research on Cancer.
   Google Scholar
• Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. 2020c. Estimated number of new cancer cases in 2020, worldwide, women all ages. In: Global Cancer Observatory: cancer Today. Lyon, France: International Agency for Research on Cancer.
   Google Scholar
• Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. 2020d. Estimated number of prevalent cases (5-year) in 2020, breast, all ages. In: Global Cancer Observatory: cancer Today. Lyon, France: International Agency for Research on Cancer.
   Google Scholar
• Ferrari RJ, Rangayyan RM, Borges RA, Frère AF. 2004. Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling. Med Biol Eng Comput. 42(3):378–387. doi:10.1007/BF02344714.
   PubMed Web of Science ®Google Scholar
• Feudjio C, Tiedeu A, Klein J, Colot O. 2017. Automatic Extraction of breast region in raw mammograms using a combined strategy. 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); Jaipur, India. IEEE. p. 158–162. doi:10.1109/SITIS.2017.35.
   Google Scholar
• Gao F, Wu T, Li J, Zheng B, Ruan L, Shang D, Patel B. 2018. SD-CNN: a shallow-deep CNN for improved breast cancer diagnosis. Comput Med Imaging Graph. 70:53–62. doi:10.1016/j.compmedimag.2018.09.004.
   PubMed Web of Science ®Google Scholar
• Gardezi SJS, Elazab A, Lei B, Wang T. 2019. Breast cancer detection and diagnosis using mammographic data: systematic review. J Med Internet Res. 21(7):e14464. doi:10.2196/14464.
   PubMed Web of Science ®Google Scholar
• Görgel P, Sertbas A, Ucan ON. 2013. Mammographical mass detection and classification using Local Seed Region Growing–Spherical Wavelet Transform (LSRG–SWT) hybrid scheme. Comput Biol Med. 43(6):765–774. doi:10.1016/j.compbiomed.2013.03.008.
   PubMed Web of Science ®Google Scholar
• Han B, Han Y, Gao X, Zhang L. 2019. Boundary constraint factor embedded localizing active contour model for medical image segmentation. J Ambient Intell Human Comput. 10(10):3853–3862. doi:10.1007/s12652-018-0978-x.
   Web of Science ®Google Scholar
• Hassanien A. 2007. Fuzzy rough sets hybrid scheme for breast cancer detection. Image Vis Comput. 25(2):172–183. doi:10.1016/j.imavis.2006.01.026.
   Web of Science ®Google Scholar
• Hazarika M, Mahanta LB. 2018. A Novel region growing based method to remove pectoral muscle from MLO mammogram images. In: Kalam A, Das S, and Sharma K, editors. Advances in Electronics, Communication and Computing. Singapore: Springer Singapore; p. 307–316. doi:10.1007/978-981-10-4765-7_32.
   Google Scholar
• Heath M, Bowyer K, Kopans D, Kegelmeyer P, Moore R, Chang K, Munishkumaran S. 1998. Current status of the digital database for screening mammography. In: Karssemeijer N, Thijssen M, Hendriks J, van Erning L. editors. Digital Mammography. Digital Mammography: Nijmegen; p. 457–460. doi:10.1007/978-94-011-5318-8_75.
   Google Scholar
• Hejazi MR, Ho YS. 2005. Automated detection of tumors in mammograms using two segments for classification. In: Ho Y-S, and Kim HJ, editors. Advances in Multimedia Information Processing - PCM 2005. Berlin Heidelberg: Springer; p. 910–921. doi:10.1007/11581772_80.
   Google Scholar
• Huo Z, Giger ML, Vyborny CJ, Bick U, Lu P, Wolverton DE, Schmidt RA. 1995. Analysis of spiculation in the computerized classification of mammographic masses. Med Phys. 22(10):1569–1579. doi:10.1118/1.597626.
   PubMed Web of Science ®Google Scholar
• Jaleel JA, Salim S, Archana S. 2014. Computer Aided Detection of solid breast nodules: performance evaluation of Support Vector Machine and K- Nearest Neighbor classifiers. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT); Kanyakumari, India. IEEE. p. 1–6. doi: 10.1109/ICCICCT.2014.6992919.
   Google Scholar
• Jiménez Gaona Y, Rodriguez-Alvarez MJ, Espino-Morato H, Castillo Malla D, Lakshminarayanan V 2021. DenseNet for Breast Tumor Classification in Mammographic Images. In: Rojas I, Castillo-Secilla D, Herrera LJ, Pomares H, editors. Bioengineering and Biomedical Signal and Image Processing. Springer International Publishing; pp. 166–176. doi:10.1007/978-3-030-88163-4_16.
   Google Scholar
• Johny A, Fernandez JJ. 2018. Breast cancer detection in mammogram using fuzzy C-means and random forest classifier. Int J Sci Res Sci Eng Technol. 4(8):312–321.
   Google Scholar
• Jothilakshmi GR, Sharmila P, Raaza A. 2016. Mammogram segmentation using region based method with split and merge technique. Indian J Sci Technol. 9(40). doi:10.17485/ijst/2016/v9i40/99589.
   Google Scholar
• Jung H, Kim B, Lee I, Yoo M, Lee J, Ham S, Woo O, Kang J, Kaderali L. 2018. Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLos One. 13(9):e0203355. doi:10.1371/journal.pone.0203355.
   PubMed Web of Science ®Google Scholar
• Kaur A, Singh Y, Neeru N, Kaur L, Singh A. 2022. A survey on deep learning approaches to medical images and a systematic look up into real-time object detection. Arch Computat Methods Eng. 29(4):2071–2111. doi:10.1007/s11831-021-09649-9.
   Web of Science ®Google Scholar
• Khan SU, Wang F, Liou Juin J, Liu Y. 2023. Segmentation of breast tumors using cutting-edge semantic segmentation models. Comput Methods Biomech Biomed Eng Imaging Vis. 11(2):242–252. doi:10.1080/21681163.2022.2064767.
   Web of Science ®Google Scholar
• Khuzi AM, Besar R, Zaki WMDW, Ahmad NN. 2009. Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomed Imaging Interv J. 5(3). doi:10.2349/biij.5.3.e17.
   Google Scholar
• Kshema GMJ, Dhas DAS. 2017. Efficient mammographic mass segmentation techniques: a review. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET); Chennai, India. IEEE. p. 2253–2256. doi: 10.1109/WiSPNET.2017.8300160.
   Google Scholar
• Lbachir IA, Daoudi I, Tallal S. 2021. Automatic computer-aided diagnosis system for mass detection and classification in mammography. Multimed Tools Appl. 80(6):9493–9525. doi:10.1007/s11042-020-09991-3.
   Web of Science ®Google Scholar
• Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. 2017. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data. 4(1):170177. doi:10.1038/sdata.2017.177.
   PubMedGoogle Scholar
• Lewis SH, Dong A. 2012. Detection of breast tumor candidates using marker-controlled watershed segmentation and morphological analysis. 2012 IEEE Southwest Symposium on Image Analysis and Interpretation; Santa Fe, NM, USA. IEEE. p. 1–4. doi: 10.1109/SSIAI.2012.6202438.
   Google Scholar
• Li H, Chen D, Nailon WH, Davies ME, Laurenson D, Kainz B, Maicas G, Martel A, Maier-Hein L, Bhatia K, et al. 2018. Improved breast mass segmentation in mammograms with conditional residual U-Net. In: Stoyanov D, Taylor Z, Beichel RR, Bradley AP, Brown MS, and Veiga C, editors Image analysis for moving organ, breast, and thoracic images. Cham: Springer International Publishing; p. 81–89.
   Google Scholar
• Lopez MAG. 2012. BCDR consortium: breast cancer digital repository. Porto, Portugal: University of Porto.
   Google Scholar
• Lotter W, Sorensen G, Cox D. 2017. A multi-scale CNN and curriculum learning strategy for mammogram classification. In: Cardoso MJ, Arbel T, Carneiro G, Syeda-Mahmood T, Tavares JMRS, Moradi M, Bradley A, Greenspan H, Papa JP, Madabhushi A, Nascimento JC, Cardoso JS, Belagiannis V, and Lu Z, editors. Deep learning in medical image analysis and multimodal learning for clinical decision. Support: Springer International Publishing; p. 169–177. doi:10.1007/978-3-319-67558-9_20.
   Google Scholar
• Ma F, Bajger M, Slavotinek JP, Bottema MJ. 2007. Two graph theory based methods for identifying the pectoral muscle in mammograms. Pattern Recogn. 40(9):2592–2602. doi:10.1016/j.patcog.2006.12.011.
   Web of Science ®Google Scholar
• Matheus BRN, Schiabel H. 2011. Online mammographic images database for development and comparison of CAD Schemes. J Digit Imaging. 24(3):500–506. doi:10.1007/s10278-010-9297-2.
   PubMed Web of Science ®Google Scholar
• Melekoodappattu JG, Subbian PS, Queen MPF. 2021. Detection and classification of breast cancer from digital mammograms using hybrid extreme learning machine classifier. Int J Imaging Syst Technol. 31(2):909–920. doi:10.1002/ima.22484.
   Web of Science ®Google Scholar
• Melouah A, Layachi S. 2015. A novel automatic seed placement approach for region growing segmentation in mammograms. Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication; Batna Algeria. p. 1–5. doi:10.1145/2816839.2816892.
   Google Scholar
• Mencattini A, Salmeri M, Casti P, Raguso G, L’Abbate S, Chieppa L, Ancona A, Mangieri F, Pepe ML. 2011. Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models. 2011 IEEE International Symposium on Medical Measurements and Applications; Bari, Italy. IEEE. p. 632–637. doi:10.1109/MeMeA.2011.5966747.
   Google Scholar
• Militello C, Rundo L, Dimarco M, Orlando A, Woitek R, D’Angelo I, Russo G, Bartolotta TV. 2022. 3D DCE-MRI radiomic analysis for malignant lesion prediction in breast cancer patients. Acad Radiol. 29(6):830–840. doi:10.1016/j.acra.2021.08.024.
   PubMed Web of Science ®Google Scholar
• Moayedi F, Azimifar Z, Boostani R, Katebi S. 2010. Contourlet-based mammography mass classification using the SVM family. Comput Biol Med. 40(4):373–383. doi:10.1016/j.compbiomed.2009.12.006.
   PubMed Web of Science ®Google Scholar
• Moghbel M, Ooi CY, Ismail N, Hau YW, Memari N. 2020. A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artif Intell Rev. 53(3):1873–1918. doi:10.1007/s10462-019-09721-8.
   Web of Science ®Google Scholar
• Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. 2012. Inbreast: toward a full-field digital mammographic database. Acad Radiol. 19(2):236–248. doi:10.1016/j.acra.2011.09.014.
   PubMed Web of Science ®Google Scholar
• Nayak T, Bhat N, Bhat V, Shetty S, Javed M, Nagabhushan P. 2019. Automatic segmentation and breast density estimation for cancer detection using an efficient watershed algorithm. In: Nagabhushan P, Guru DS, Shekar BH, and Kumar YHS, editors. Data Analytics and Learning. Springer Singapore; p. 347–358. doi:10.1007/978-981-13-2514-4_29.
   Google Scholar
• Nedra A, Shoaib M, Gattoufi S. 2018. Detection and classification of the breast abnormalities in digital mammograms via linear support vector machine. Middle East Conference On Biomedical Engineering, MECBME. 2018:141–146. doi:10.1109/MECBME.2018.8402422.
   Google Scholar
• Nemade V, Pathak S, Dubey AK. 2022. A Systematic literature review of breast cancer diagnosis using machine intelligence techniques. Arch Computat Methods Eng. 29(6):4401–4430. doi:10.1007/s11831-022-09738-3.
   Web of Science ®Google Scholar
• Nithya R, Santhi B. 2015. Computer aided diagnosis system for mammogram analysis: a survey. J Med Imaging Hlth Inform. 5(4):653–674. doi:10.1166/jmihi.2015.1441.
   Web of Science ®Google Scholar
• Oliver A, Freixenet J, Martí J, Pérez E, Pont J, Denton ERE, Zwiggelaar R. 2010. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal. 14(2):87–110. doi:10.1016/j.media.2009.12.005.
   PubMed Web of Science ®Google Scholar
• Oliver A, Freixenet J, MartÍ R, Pont J, Pérez E, Denton ERE, Zwiggelaar R. 2008. A novel breast tissue density classification methodology. IEEE Trans Inform Technol Biomed. 12(1):55–65. doi:10.1109/TITB.2007.903514.
   PubMed Web of Science ®Google Scholar
• Oliver A, Freixenet J, Martí R, Zwiggelaar R. 2006. A comparison of breast tissue classification techniques. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2006: 9th International Conference. Proceedings, Part II 9; Copenhagen, Denmark. Berlin, Heidelberg: Springer; p. 872–879.
   Google Scholar
• Oliver A, Freixenet J, Zwiggelaar R. 2005. Automatic classification of breast density. IEEE International Conference On Image Processing 2005. 2:II–1258. doi:10.1109/ICIP.2005.1530291.
   Google Scholar
• Oliver A, Tortajada M, Lladó X, Freixenet J, Ganau S, Tortajada L, Vilagran M, Sentís M, Martí R. 2015. Breast density analysis using an automatic density segmentation algorithm. J Digit Imaging. 28(5):604–612. doi:10.1007/s10278-015-9777-5.
   PubMed Web of Science ®Google Scholar
• Park H, Lim Y, Ko ES, Cho H, Lee JE, Han BK, Ko EY, Choi JS, Park KW. 2018. Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res. 24(19):4705–4714. doi:10.1158/1078-0432.CCR-17-3783.
   PubMed Web of Science ®Google Scholar
• Patel BC, Sinha GR. 2010. An adaptive K-means clustering algorithm for breast image segmentation. IJCA. 10(4):35–38. doi:10.5120/1467-1982.
   Google Scholar
• Patil RS, Biradar N. 2020. Improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifier. IJICC. 13(2):181–205. doi:10.1108/IJICC-10-2019-0116.
   Google Scholar
• Pei C, Wang C, Xu S. 2010. Segmentation of the breast region in mammograms using marker-controlled watershed transform. The 2nd International Conference on Information Science and Engineering; Hangzhou, China. IEEE. p. 2371–2374. doi: 10.1109/ICISE.2010.5690735.
   Google Scholar
• Petrick N, Chan H, Sahiner B, Helvie MA. 1999. Combined adaptive enhancement and region‐growing segmentation of breast masses on digitized mammograms. Med Phys. 26(8):1642–1654. doi:10.1118/1.598658.
   PubMed Web of Science ®Google Scholar
• Petrick N, Sahiner B, Chan HP, Helvie MA, Paquerault S, Hadjiiski LM. 2002. Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis—experience in 263 patients. Radiology. 224(1):217–224. doi:10.1148/radiol.2241011062.
   PubMed Web of Science ®Google Scholar
• Raba D, Oliver A, Martí J, Peracaula M, Espunya J. 2005. Breast segmentation with pectoral muscle suppression on digital mammograms. In: Marques JS, Pérez de la Blanca N, and Pina P, editors. Pattern Recognition and Image Analysis. Berlin Heidelberg: Springer; p. 471–478. doi:10.1007/11492542_58.
   Google Scholar
• Rabottino G, Mencattini A, Salmeri M, Caselli F, Lojacono R. 2011. Performance evaluation of a region growing procedure for mammographic breast lesion identification. Comput Stand Interfaces. 33(2):128–135. doi:10.1016/j.csi.2010.06.003.
   Web of Science ®Google Scholar
• Rajalakshmi NR, Vidhyapriya R, Elango N, Ramesh N. 2021. Deeply supervised u-net for mass segmentation in digital mammograms. Int J Imaging Syst Technol. 31(1):59–71. doi:10.1002/ima.22516.
   Web of Science ®Google Scholar
• Raj SPS, Raja NSM, Madhumitha MR, Rajinikanth V. 2018. Examination of digital mammogram using otsu’s function and watershed segmentation. 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII); Chennai, India. IEEE. p. 206–212. doi:10.1109/ICBSII.2018.8524794.
   Google Scholar
• Raman V, Sumari P, Then P. 2011. Matab implementation and results of region growing segmentation using haralic texture features on mammogram mass segmentation. Advances in Wireless, Mobile Networks and Applications: International Conferences, WiMoA 2011 and ICCSEA 2011; Dubai, United Arab Emirates. Berlin, Heidelberg: Springer. p. 293–303. doi:10.1007/978-3-642-21153-9.
   Google Scholar
• Rangayyan RM, Ayres FJ, Leo Desautels JE. 2007. A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Franklin Inst. 344(3):312–348. doi:10.1016/j.jfranklin.2006.09.003.
   Web of Science ®Google Scholar
• Rangayyan RM, El-Faramawy NM, Desautels JEL, Alim OA. 1997. Measures of acutance and shape for classification of breast tumors. IEEE Trans Med Imaging. 16(6):799–810. doi:10.1109/42.650876.
   PubMed Web of Science ®Google Scholar
• Ranjbarzadeh R, Tataei Sarshar N, Jafarzadeh Ghoushchi S, Saleh Esfahani M, Parhizkar M, Pourasad Y, Anari S, Bendechache M. 2022. MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network. Ann Oper Res. doi:10.1007/s10479-022-04755-8.
   Web of Science ®Google Scholar
• Ravishankar M, Varalatchoumy M 2017. Four novel approaches for detection of region of interest in mammograms — a comparative study. 2017 International Conference on Intelligent Sustainable Systems (ICISS); Palladam, India. IEEE. p. 261–265. doi:10.1109/ISS1.2017.8389410.
   Google Scholar
• Rojas Domínguez A, Nandi AK. 2009. Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Pattern Recogn. 42(6):1138–1148. doi:10.1016/j.patcog.2008.08.006.
   Web of Science ®Google Scholar
• Rouhi R, Jafari M, Kasaei S, Keshavarzian P. 2015. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl. 42(3):990–1002. doi:10.1016/j.eswa.2014.09.020.
   Web of Science ®Google Scholar
• Sahiner B, Petrick N, Chan HP, Hadjiiski LM, Paramagul C, Helvie MA, Gurcan MN. 2001. Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization. IEEE Trans Med Imaging. 20(12):1275–1284. doi:10.1109/42.974922.
   PubMed Web of Science ®Google Scholar
• Saleck MM, El Moutaouakkil A, Rmili M. 2018. Hybrid clustering and texture features in segmentation of breast masses in mammograms. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON); Vancouver, BC, Canada. p. 992–995. doi:10.1109/IEMCON.2018.8614906.
   Google Scholar
• Saltanat N, Hossain MA, Alam MS 2010. An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms. 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA); Changsha, China. IEEE. p. 1510–1517. doi:10.1109/BICTA.2010.5645272.
   Google Scholar
• Santos VT, Schiabel H, Goes CE, Benatti RH. 2002. A segmentation technique to detect masses in dense breast digitized mammograms. J Digit Imaging. 15:210–213. doi:10.1007/s10278-002-5071-4.
   PubMedGoogle Scholar
• Schiabel H, Santos VT, Angelo MF. 2008. Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography CAD schemes. Proceedings of the 2008 ACM Symposium on Applied Computing; Fortaleza, Ceara, Brazil. p. 1333–1337.
   Google Scholar
• Shen T, Gou C, Wang J, Wang FY. 2020. Simultaneous segmentation and classification of mass region from mammograms using a mixed-supervision guided deep model. IEEE Signal Process Lett. 27:196–200. doi:https://doi.org/10.1109/LSP.2019.2963151.
   Web of Science ®Google Scholar
• Shi J, Sahiner B, Chan H, Ge J, Hadjiiski L, Helvie MA, Nees A, Wu Y, Wei J, Zhou C, et al. 2008. Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med Phys. 35(1):280–290. doi:10.1118/1.2820630.
   PubMed Web of Science ®Google Scholar
• Shrivastava N, Bharti J. 2020. Automatic seeded region growing image segmentation for medical image segmentation: a brief review. Int J Image Grap. 20(3):2050018. doi:10.1142/S0219467820500187.
   Web of Science ®Google Scholar
• Shrivastava N, Bharti J. 2022. Breast tumor detection in digital mammogram based on efficient seed region growing segmentation. IETE J Res. 68(4):2463–2475. doi:10.1080/03772063.2019.1710583.
   Web of Science ®Google Scholar
• Singh VK, Romani S, Rashwan HA, Akram F, Pandey N, Sarker MK, Abdulwahab S, Torrents-Barrena J, Saleh A, Arquez M, et al. 2018. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing; p. 833–840. doi:10.1007/978-3-030-00934-2_92.
   Google Scholar
• Smith AP, Hall PA, Marcello DM. 2005. Emerging technologies in breast cancer detection. Radiol Manage. 26(M):16–24. http://www.ncbi.nlm.nih.gov/pubmed/15377106
   Google Scholar
• Sonar P, Bhosle U, Choudhury C. 2017. Mammography classification using modified hybrid SVM-KNN. 2017 International Conference on Signal Processing and Communication (ICSPC); Coimbatore, India. IEEE. p. 305–311. doi: 10.1109/CSPC.2017.8305858.
   Google Scholar
• Song E, Jiang L, Jin R, Zhang L, Yuan Y, Li Q. 2009. Breast mass segmentation in mammography using plane fitting and dynamic programming. Acad Radiol. 16(7):826–835. doi:10.1016/j.acra.2008.11.014.
   PubMed Web of Science ®Google Scholar
• Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S. 2015. Mammographic image analysis society (mias) database v1.21. Apollo - University of Cambridge Repository.
   Google Scholar
• Suliga M, Deklerck R, Nyssen E. 2008. Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Comput Med Imaging Graph. 32(6):502–512. doi:10.1016/j.compmedimag.2008.05.004.
   PubMed Web of Science ®Google Scholar
• Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. 2021. Global cancer statistics 2020: gLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin. 71(3):209–249. doi:10.3322/caac.21660.
   PubMed Web of Science ®Google Scholar
• Taghanaki SA, Liu Y, Miles B, Hamarneh G. 2017. Geometry-based pectoral muscle Segmentation from MLO mammogram views. IEEE Trans Biomed Eng. 64(11):2662–2671. doi:10.1109/TBME.2017.2649481.
   PubMed Web of Science ®Google Scholar
• Tang J, Rangayyan RM, Xu J, Naqa IE, Yang Y. 2009. Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inform Technol Biomed. 13(2):236–251. doi:10.1109/TITB.2008.2009441.
   PubMedGoogle Scholar
• Te Brake GM, Karssemeijer N. 2001. Segmentation of suspicious densities in digital mammograms. Med Phys. 28(2):259–266. doi:10.1118/1.1339884.
   PubMed Web of Science ®Google Scholar
• Timp S, Karssemeijer N. 2004. A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med Phys. 31(5):958–971. doi:10.1118/1.1688039.
   PubMed Web of Science ®Google Scholar
• Todua F, Gagua R, Maglakelidze M, Maglakelidze D. 2015. Cancer incidence and mortality - Major patterns in GLOBOCAN 2012, worldwide and Georgia. Bulletin Of The Georgian National Academy Of Sciences. 9(1):168–173. doi:10.1002/ijc.29210.
   Google Scholar
• Torrent A, Bardera A, Oliver A, Freixenet J, Boada I, Feixes M, Martí R, Lladó X, Pont J, Pérez E, et al. 2008. Breast density segmentation: a comparison of clustering and region based techniques. In: Krupinski EA, editor. Digital Mammography. Berlin Heidelberg: Springer Berlin Heidelberg; p. 9–16. doi: 10.1007/978-3-540-70538-3_2.
   Google Scholar
• Valdés-Santiago D, Quintana-Martínez R, León-Mecías Á, Díaz-Romañach M L B. 2018. Mammographic mass segmentation using fuzzy C–means and decision trees. In: Perales F, and Kittler J, editors. Articulated motion and deformable objects. Springer International Publishing; p. 1–10. doi:10.1007/978-3-319-94544-6_1.
   Google Scholar
• Wang Z, Yu G, Kang Y, Zhao Y, Qu Q. 2014. Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing. 128:175–184. doi:10.1016/j.neucom.2013.05.053.
   Web of Science ®Google Scholar
• Wang L, Zhu M, Deng L, Yuan X. 2010. Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model. J Zhejiang Univ Sci C. 11(2):111–118. doi:10.1631/jzus.C0910025.
   Web of Science ®Google Scholar
• Wei J, Chan H, Sahiner B, Hadjiiski LM, Helvie MA, Roubidoux MA, Zhou C, Ge J. 2006. Dual system approach to computer‐aided detection of breast masses on mammograms. Med Phys. 33(11):4157–4168. doi:10.1118/1.2357838.
   PubMed Web of Science ®Google Scholar
• Wei CH, Chen SY, Liu X. 2012. Mammogram retrieval on similar mass lesions. Comput Methods Programs Biomed. 106(3):234–248. doi:10.1016/j.cmpb.2010.09.002.
   PubMed Web of Science ®Google Scholar
• Wei K, Guangzhi W, Hui D. 2005. Segmentation of the breast region in mammograms using watershed transformation. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference; Shanghai. IEEE. p. 6500–6503. doi: 10.1109/IEMBS.2005.1615988.
   Google Scholar
• Wirtti TT, Salles EOT. 2011. Segmentation of masses in digital mammograms. ISSNIP Biosignals and Biorobotics Conference 2011; Vitoria, Brazil. IEEE. P. 1–7. doi: 10.1109/BRC.2011.5740680.
   Google Scholar
• Xu S, Liu H, Song E. 2011. Marker-controlled watershed for lesion segmentation in mammograms. J Digit Imaging. 24(5):754–763. doi:10.1007/s10278-011-9365-2.
   PubMed Web of Science ®Google Scholar
• Yuan Y, Giger ML, Li H, Suzuki K, Sennett C. 2007. A dual‐stage method for lesion segmentation on digital mammograms. Med Phys. 34(11):4180–4193. doi:10.1118/1.2790837.
   PubMed Web of Science ®Google Scholar
• Zeiser FA, da Costa CA, Zonta T, Marques NMC, Roehe AV, Moreno M, da Rosa Righi R. 2020. Segmentation of masses on mammograms using data augmentation and deep Learning. J Digit Imaging. 33(4):858–868. doi:10.1007/s10278-020-00330-4.
   PubMed Web of Science ®Google Scholar
• Zhang H, Foo SW, Krishnan SM, Thng CH. 2004. Automated breast masses segmentation in digitized mammograms. IEEE International Workshop on Biomedical Circuits and Systems 2004; Singapore. IEEE. p. S2/2–S1. doi: 10.1109/BIOCAS.2004.1454102.
   Google Scholar
• Zhu W, Xiang X, Tran TD, Hager GD, Xie X. 2018. Adversarial deep structured nets for mass segmentation from mammograms. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018); Washington, DC, USA. IEEE. p. 847–850. doi:10.1109/ISBI.2018.8363704.
   Google Scholar
• Zhu W, Zeng N, Wang N. 2010. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG Proceedings: Health Care and Life Sciences; 2010 Nov 14–17; Baltimore, Maryland. p. 67.
   Google Scholar