5775a97f-41d6-400f-b009-8d02780c3f05 beie director@blueeyesintelligence.org 10.35940/ijitee.G9054.0510721 Journal of Digital ImagingAgner24446201110.1007/s10278-010-9298-1Textural kinetics: A novel dynamic contrast- enhanced(dce)-mri feature for breast lesion classification1. Agner, S.C., Soman, S., Libfeld, E., Mcdonald, M., Thomas, K., Englander, S., Rosen, M.A., Chin, D., Nosher, J., Madabhushi, A.: Textural kinetics: A novel dynamic contrast- enhanced(dce)-mri feature for breast lesion classification. Journal of Digital Imaging 24, 446-63 (2011)10.1109/ICCKE48569.2019.89647942. Arjmand, A., Meshgini, S., Afrouzian, R., Farzamnia, A.: Breast tumor segmentation using k-means clustering and cuckoo search optimization. IEEE (2019)Journal of Medical Signals SensorsAzmi1156201110.4103/2228-7477.95284A new markov random field segmentation method for breast lesion segmentation in mr images3. Azmi, R., Norozi, N.: A new markov random field segmentation method for breast lesion segmentation in mr images. Journal of Medical Signals Sensors 1, 156-164 (2011)10.1007/s10462-010-9169-74. Balafar, M., Ramli, A., Mashohor, S.: A new method for mr grayscale inhomogeneity correction. Artif. Intell. Rev. 34, 195-204 (2010)5. Bohare, M., Cheeran, A., Sarode, V.: Analysis of breast mri images using wavelets for detection of cancer. IJCA Special Issue on Electronics, Information and Communication Engineering 4, 1-3 (2011)European Journal of UltrasoundBoukerroui8135199810.1016/S0929-8266(98)00062-7Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation6. Boukerroui, D., Basset, O., Guerin, N., Baskurt, A.: Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. European Journal of Ultrasound 8, 135-144 (1998)10.1002/ijc.277117. Bray, F., Ren, J.S., Masuyer, E., Ferlay, J.: Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer 132(5), 1133--45 (2013). DOI 10.1002/ijc.27711Magnetic Resonance ImagingChang303312201210.1016/j.mri.2011.12.002Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering8. Chang, Y.C., Huang, Y.H., Huang, C.S., Chang, P.K., Chen, J.H., Chang, R.F.: Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering. Magnetic Resonance Imaging 30(3), 312-322 (2012)10.1016/j.acra.2005.08.0359. Chen, W., Giger, M.L., Bick, U.: A fuzzy c-means (fcm)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced mr images. European Journal of Ultrasound 1, 63-72 (2006)10. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F.: The cancer imaging archive (tcia): Maintaining and operating a public information repositoryMedical physicsCui364359200910.1118/1.3213514Malignant lesion segmentation in contrast-enhanced breast mr images based on the marker-controlled watershed11. Cui, Y., Tan, Y., Zhao, B., Liberman, L., Parbhu, R., Kaplan, J., Theodoulou, M., Hudis, C., Schwartz, L.: Malignant lesion segmentation in contrast-enhanced breast mr images based on the marker-controlled watershed. Medical physics 36, 4359-69 (2009)10.1109/TPAMI.1979.476690912. Davies, D., Bouldin, D.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224-227 (1979)Swarm and Evolutionary ComputationDerrac13201110.1016/j.swevo.2011.02.002A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms13. Derrac, J., Garc'la, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1, 3-18 (2011)14. Ferlay, J., Soerjomataram, I., Ervik, M.: Globocan 2012 v1.0, cancer incidence and mortality worldwide: Iarc cancerbase. 11. GLOBOCAN (2013)15. Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D., Forman, D., Bray, F.: Globocan 2012 v1.0, cancer incidence and mortality worldwide: Iarc cancer base. no. 11 [internet]. In: F. Lyon (ed.) International Agency for Research on CancerMedical Image AnalysisHamy18301201410.1016/j.media.2013.10.016Respiratory motion correc- tion in dynamic mri using robust data decomposition registration - application to dce-mri16. Hamy, V., Dikaios, N., Punwani, S., Melbourne, A., Latifoltojar, A., Makanyanga, J., Chouhan, M., Helbren, E., Menys, A., Taylor, S., Atkinson, D.: Respiratory motion correc- tion in dynamic mri using robust data decomposition registration - application to dce-mri. Medical Image Analysis 18, 301-313 (2014)Clinic ImagingHauth30160200610.1016/j.clinimag.2005.11.005Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced mr mammography17. Hauth, E., Stockamp, C., Maderwald, S., Mu¨hler, A., Kimmig, R., Jaeger, H., Barkhausen, J., Forsting, M.: Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced mr mammography. Clinic Imaging 30, 160-165 (2006)Com- puterized Medical Imaging and GraphicsJayender37281201310.1016/j.compmedimag.2013.04.003Sta- tistical learning algorithm for in situ and invasive breast carcinoma segmentation18. Jayender, J., Gombos, E., Chikarmane, S., Dabydeen, D., Jolesz, F., Vosburgh, K.: Sta- tistical learning algorithm for in situ and invasive breast carcinoma segmentation. Com- puterized Medical Imaging and Graphics 37, 281-292 (2013)IEEE Transactions on medical imagingKhalvati34116201510.1109/TMI.2014.2347703Automated segmentation of breast in 3d mr images using a robust atlas19. Khalvati, F., Ortiz, C.G., Balasingham, S., Martel, A.L.: Automated segmentation of breast in 3d mr images using a robust atlas. IEEE Transactions on medical imaging 34, 116-125 (2015)20. Lingle, W., Erickson, B.J., Zuley, M.L., Jarosz, R., Bonaccio, E., Filippini, J., N., G.: Radiology data from the cancer genome atlas breast invasive carcinoma collection [tcga- brca] (2007)21. MacQueen, J.: Some methods for classification and analysis of multivariate observations. p. 281-297. University of California Press (1967)10.1002/jmri.2422922. McClymont, D., Mehnert, A., Trakic, A., Kennedy, D., Crozier, S.: 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 (2014)Artificial Intelligence in MedicineMilenkovi'c582101201310.1016/j.artmed.2013.03.002Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions23. Milenkovi'c, J., Hertl, K., Koˇsir, A., Zˇibert, J., Tasiˇc, J.: Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions. Artificial Intelligence in Medicine 58(2), 101-114 (2013)24. Mlejnek, M., Ermes, P., Vilanova, A., Rijt, R., Bosch, H., Gerritsen, F., Gr¨oller, E.: Application-oriented extensions of profile flags (2006)Biomedical Signal Processing and ControlMohan956201410.1016/j.bspc.2013.10.007A survey on the magnetic resonance image denoising methods25. Mohan, J., Krishnavenib, V., Guo, Y.: A survey on the magnetic resonance image denoising methods. Biomedical Signal Processing and Control 9, 56-69 (2014)10.1016/j.acra.2008.06.00526. Nie, K., Chen, J., Yu, H., Chu, Y., Nalcioglu, O., Su, M.: Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast mri. Acad. Radiol. 15, 1513-1525 (2008)10.1109/34.5620527. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffussion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629-639 (1990)10.1109/CBMS.2019.0013028. Piantadosi, G., Marrone, S., Galli, A., Sansone, M., Sansone, C.: Dce-mri breast lesions segmentation with a 3tp u-net deep convolutional neural network. IEEE (2019)10.1007/978-1-4939-1267-429. Shetty, M.: Breast Cancer Screening and Diagnosis - A Synopsis. Springer Science + Business Media, New York (2015)30. Si, T.: Grammatical evolution using fireworks algorithm. In: M. Pant, K. Deep, J. Bansal, A. Nagar, K. Das (eds.) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol. 436. Singapore (2016)10.1109/ICCPCT.2014.705505131. Si, T., De, A., Bhattacharjee, A.: Brain mri segmentation for tumor detection using gram- matical swarm based clustering algorithm. In: Proc. of IEEE International Conference on Circuits, Power and Computing Technologies (ICCPCT-2014), vol. 436. Nagercoil, India (2014)International Journal of Computer ApplicationsSi12141201510.5120/21525-4481Grammatical swarm based segmentation methodology for lesion segmentation in brain mri32. Si, T., De, A., Bhattacharjee, A.: Grammatical swarm based segmentation methodology for lesion segmentation in brain mri. International Journal of Computer Applications 121(4), 1-8 (2015)Computers in Biology and MedicineSim4946201410.1016/j.compbiomed.2014.03.003Breast cancer detection from mr images through an auto-probing discrete fourier transform system33. Sim, K., Chia, F., Nia, M., Tso, C., Chong, A., Abbas, S., Chong, S.: Breast cancer detection from mr images through an auto-probing discrete fourier transform system. Computers in Biology and Medicine 49, 46-59 (2014)10.1007/978-3-642-13495-1_4434. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Y. Tan, Y. ShiKay, C. Tan (eds.) ICSI 2010, Part I, LNCS, vol. 6145, p. 355-364. Springer, Heidelberg (2010)IEEE Transactions on Biomedical EngineeringTuncay62796305688201510.1109/TBME.2014.2364015Realistic microwave breast models through t1-weighted 3-d mri data35. Tuncay, A.H., Akduman, I.: Realistic microwave breast models through t1-weighted 3-d mri data. IEEE Transactions on Biomedical Engineering 62(796305), 688-698 (2015)10.1016/j.acra.2012.08.01636. Wang, Y., Morrell, G., Heibrun, M., Payne, A., Parker, D.: 3d multi-parametric breast mri segmentation using hierarchical support vector machine with coil sensitivity correction. Acad. Radiol. 20, 137-147 (2013)10.1016/j.ejrad.2011.12.02937. Wei, C.H., Li, Y., Huang, P., Gwo, C.Y., Harms, S.: Estimation of breast density: An adaptive moment preserving method for segmentation of fibroglandular tissue in breast magnetic resonance images. European Journal of Radiology 81(4), e618-e624 (2012)38. WHO https://www.who.int/cancer/prevention/diagnosis- screening/breast-cancer/en/. Accessed online: 05/12/201910.1117/12.65430839. Wu, Q., Salganicoff, M., Krishnan, A., Fussell, D.S., Markey, M.K.: Interactive lesion segmentation on dynamic contrast enhanced breast mr using a markov model. Proc. SPIE (6144) (2006)IEEE Journal of selected topics in signal processingYao394200910.1109/JSTSP.2008.2011110Breast tumor analysis in dynamic contrast enhanced mri using texture features and wavelet transform40. Yao, J., Chen, J., Chow, C.: Breast tumor analysis in dynamic contrast enhanced mri using texture features and wavelet transform. IEEE Journal of selected topics in signal processing 3, 94-100 (2009)