{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T04:09:16Z","timestamp":1780114156255,"version":"3.54.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,1,2]],"date-time":"2019-01-02T00:00:00Z","timestamp":1546387200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"published-print":{"date-parts":[[2019,2]]},"DOI":"10.1007\/s10916-018-1135-y","type":"journal-article","created":{"date-parts":[[2019,1,2]],"date-time":"2019-01-02T17:27:09Z","timestamp":1546450029000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Efficient Segmentation of Brain Tumor Using FL-SNM with a Metaheuristic Approach to Optimization"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3291-7249","authenticated-orcid":false,"given":"Aparna","family":"Natarajan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sathiyasekar","family":"Kumarasamy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,2]]},"reference":[{"key":"1135_CR1","doi-asserted-by":"crossref","unstructured":"Dawngliana, M., Deb, D., Handique, M. and Roy, S., Automatic brain tumour segmentation in mri; hybridized multilevel thresholding and level set. International Symposium on Advanced Computing and Communication, 219\u2013223, 2015.","DOI":"10.1109\/ISACC.2015.7377345"},{"key":"1135_CR2","doi-asserted-by":"crossref","unstructured":"Zabir, I., Paul, S., Rayhan, M. A., Sarker, T., Fattah, S. A., and Shahnaz, C., Automatic brain tumor detection and segmentation from multi-modal MRI images based on region growing and level set evolution. IEEE International WIE Conference on Electrical and. Comput. Eng.:503\u2013506, 2015.","DOI":"10.1109\/WIECON-ECE.2015.7443979"},{"key":"1135_CR3","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1109\/83.661179","volume":"7","author":"G-H Cottet","year":"1998","unstructured":"Cottet, G.-H., and Ayyadi, M. E. A., Volterra type model for image processing. IEEE Trans. Image Process. 7:292\u2013303, 1998.","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"1135_CR4","first-page":"1218","volume":"2","author":"J Acharya","year":"2013","unstructured":"Acharya, J., Gadhiya, S., and Raviya, K., Segmentation techniques for image analysis: a review. Int J ComputSci Manage Res 2(4):1218\u20131221, 2013.","journal-title":"Int J ComputSci Manage Res"},{"issue":"3","key":"1135_CR5","first-page":"3289","volume":"5","author":"D Naik","year":"2014","unstructured":"Naik, D., and Shah, P., A review on image segmentation clustering algorithms. Int J ComputSci Inform Technol 5(3):3289\u20133293, 2014 ISSN: 0975-9646.","journal-title":"Int J ComputSci Inform Technol"},{"issue":"1","key":"1135_CR6","doi-asserted-by":"publisher","first-page":"34","DOI":"10.21917\/ijivp.2010.0007","volume":"1","author":"SA Christe","year":"2010","unstructured":"Christe, S. A., Malathy, K., and Kandaswamy, A., Improved hybrid segmentation of brain MRI tissue and tumor using statistical features. ICTACT J. Image Video Process 1(1):34\u201349, 2010.","journal-title":"ICTACT J. Image Video Process"},{"issue":"2","key":"1135_CR7","first-page":"109","volume":"5","author":"GK Seerha","year":"2013","unstructured":"Seerha, G. K., and Kaur, R., Review on recent image segmentation techniques. Int J. ComputSci Eng (IJCSE) 5(2):109\u2013112, 2013 ISSN: 0975-3397.","journal-title":"Int J. ComputSci Eng (IJCSE)"},{"key":"1135_CR8","doi-asserted-by":"crossref","unstructured":"Liu, J. and Guo L., A New Brain MRI Image Segmentation Strategy Based on Wavelet Transform and K-means Clustering. IEEE International Conference on Signal Pro-cessing, Communications and Computing (ICSPCC), 1\u20134, 2015.","DOI":"10.1109\/ICSPCC.2015.7338884"},{"issue":"1","key":"1135_CR9","first-page":"66","volume":"3","author":"R Dass","year":"2012","unstructured":"Dass, R., and Priyanka Devi, S., Image segmentation techniques. Int J. Electron Commun Technol. 3(1):66\u201370, 2012 ISSN: 2230-7109 (Online).","journal-title":"Int J. Electron Commun Technol."},{"key":"1135_CR10","unstructured":"Amin, S. A. and Megeed, M.A., Brain tumour diagnosis systems based on artificial neural networks and segmentation using MRI. The 8th International Conference on INFOmatics and systems, 119\u2013124, 2012."},{"key":"1135_CR11","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/978-3-319-47952-1_19","volume":"530","author":"R Hiralal","year":"2016","unstructured":"Hiralal, R., and Menon, H. P., A survey of brain MRI image segmentation methods and the issues involved. Advances in Intelligent Systems and Computing 530:245\u2013259, 2016. \n                    https:\/\/doi.org\/10.1007\/978-3-319-47952-1_19\n                    \n                   Springer International Publishing.","journal-title":"Advances in Intelligent Systems and Computing"},{"issue":"5","key":"1135_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S0219691315500393","volume":"13","author":"T Si","year":"2015","unstructured":"Si, T., De, A., and Bhattacharjee, A. K., Brain MRI segmentation for tumor detection via entropy maximization using Grammatical Swarm. Int. J. Wavelets Multiresolution Inf. Process. 13(5):1\u20138, 2015. \n                    https:\/\/doi.org\/10.1142\/S0219691315500393\n                    \n                  .","journal-title":"Int. J. Wavelets Multiresolution Inf. Process."},{"key":"1135_CR13","unstructured":"Elsayad, A.M., Classification of breast cancer database using learning vector quantization neural networks. Saudi Association of Health Informatics, 1\u20139, 2014; \n                    https:\/\/www.researchgate.net\/publication\/242616752"},{"key":"1135_CR14","doi-asserted-by":"publisher","first-page":"5526","DOI":"10.1016\/j.eswa.2014.01.021","volume":"41","author":"A El-Sayed","year":"2014","unstructured":"El-Sayed, A., El-Dahshan, E. S. A., Mohsen, H. M., Revett, K., and Salem, A. B. M., Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst. Appl. 41:5526\u20135545, 2014. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2014.01.021\n                    \n                  .","journal-title":"Expert Syst. Appl."},{"key":"1135_CR15","volume-title":"Neural Networks and Learning Machines, 3rd Edition","author":"S Hyakin","year":"2011","unstructured":"Hyakin, S., Neural Networks and Learning Machines, 3rd Edition. Upper Saddle River: Pearson Prentice Hall, 2011."},{"key":"1135_CR16","unstructured":"Ortiz, A., Gorriz, J. M., Ramirez, J. and Salas-Gonzalez, D., Unsupervised Neural Techniques Applied to MR Brain Image Segmentation, Advances in Artificial Neural Systems. Hindawi Publishing Corporation. 457590:7, 2012. 10.1155\/2012\/457590."},{"key":"1135_CR17","doi-asserted-by":"publisher","first-page":"10049","DOI":"10.1016\/j.eswa.2011.02.012","volume":"38","author":"Y Zhang","year":"2011","unstructured":"Zhang, Y., Dong, Z., Wua, L., and Wanga, S., A hybrid method for MRI brain image classification. Expert Syst. Appl. 38:10049\u201310053, 2011. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2011.02.012\n                    \n                  .","journal-title":"Expert Syst. Appl."},{"key":"1135_CR18","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1109\/TST.2014.6961028","volume":"19","author":"J Liu","year":"2014","unstructured":"Liu, J., Li, M., Wang, J., Wu, F., Liu, T., and Pan, Y. A., Survey of MRI-Based Brain Tumour Segmentation Methods. Tsinghua Sci. Technol. 19:578\u2013595, 2014.","journal-title":"Tsinghua Sci. Technol."},{"key":"1135_CR19","doi-asserted-by":"publisher","unstructured":"Sharma, M., and Mukharjee, S., Brain tumor segmentation using hybrid genetic algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS). International Journal of Fuzzy Logic Systems (IJFLS) 2(4):31\u201342, 2012. \n                    https:\/\/doi.org\/10.5121\/ijfls.2012.240331\n                    \n                  .","DOI":"10.5121\/ijfls.2012.240331"},{"issue":"4","key":"1135_CR20","first-page":"1793","volume":"2","author":"T Logeswari","year":"2010","unstructured":"Logeswari, T., and Karnan, M., An enhanced implementation of brain tumor detection using segmentation based on soft computing, IACSIT'10. International Journal of Computer Theory and Engineering 2(4):1793\u20138201, 2010 586-590.","journal-title":"International Journal of Computer Theory and Engineering"},{"issue":"5","key":"1135_CR21","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira, S., Pinto, A., Alves, V., and Silva, C. A., Brain tumor segmentation using Convolutional Neural Networks in MRI images. IEEE Trans. Med. Imaging 35(5):1240\u20131251, 2016. \n                    https:\/\/doi.org\/10.1109\/TMI.2016.2538465\n                    \n                  .","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1135_CR22","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., and Larochelle, H., Brain tumor segmentation with deep neural networks. Med. Image Anal. 35:18\u201331, 2017. \n                    https:\/\/doi.org\/10.1016\/j.media.2016.05.004\n                    \n                  .","journal-title":"Med. Image Anal."},{"key":"1135_CR23","volume-title":"Anisotropic Diffusion in Image Processing","author":"J Weickert","year":"1998","unstructured":"Weickert, J., Anisotropic Diffusion in Image Processing. Stuttgart: BG. Teubner, 1998."},{"issue":"1","key":"1135_CR24","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/TIP.2014.2371244","volume":"24","author":"G Ramos-Llord\u00e9n","year":"2015","unstructured":"Ramos-Llord\u00e9n, G., Vegas-S\u00e1nchez-Ferrero G, Martin-Fernandez M, Alberola-L\u00f3pez C and Aja-Fern\u00e1ndez S. Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Trans. Image Process. 24(1):345\u2013358, 2015.","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"1135_CR25","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/36.103288","volume":"29","author":"YE Shimabukuro","year":"1991","unstructured":"Shimabukuro, Y. E., and Smith, J. A., The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans on Geoscience and Remote Sensing 29(1):16\u201320, 1991.","journal-title":"IEEE Trans on Geoscience and Remote Sensing"},{"key":"1135_CR26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815706","volume-title":"Spiking neuron models","author":"W Gerstner","year":"2002","unstructured":"Gerstner, W., and Kistler, W. M., Spiking neuron models. Cambridge: Cambridge University Press, 2002."},{"key":"1135_CR27","doi-asserted-by":"publisher","unstructured":"Meftah, B., Lezoray, O., Benyettou, A., Segmentation and Edge Detection Based on Spiking Neural Network Model Neural Process Lett published on august 20, 2010. \n                    https:\/\/doi.org\/10.1007\/s11063-010-9149-6\n                    \n                  .","DOI":"10.1007\/s11063-010-9149-6"},{"key":"1135_CR28","volume-title":"The Organization of Behavior","author":"DO Hebb","year":"1949","unstructured":"Hebb, D. O., The Organization of Behavior. New York: Wiley and Sons, 1949."},{"key":"1135_CR29","volume-title":"Segmentation with Spiking Neuron Network, CP1019, Intelligent Systems and Automation, 1st Mediterranean conference","author":"B Meftah","year":"2008","unstructured":"Meftah, B., Benyettou, A., Lezoray, O., and Debakla Image, M., Segmentation with Spiking Neuron Network, CP1019, Intelligent Systems and Automation, 1st Mediterranean conference. College Park: American Institute of Physics, 2008."},{"key":"1135_CR30","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Wang, S., Ji, G., and Dong, Z., An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine. Hindawi Publishing Corporation. Sci. World J.:130134, 2013. \n                    https:\/\/doi.org\/10.1155\/2013\/130134\n                    \n                  .","DOI":"10.1155\/2013\/130134"},{"key":"1135_CR31","doi-asserted-by":"publisher","unstructured":"Meng, X., Liu, Y., Gao, X. and Zhang, H., A new bio-inspired algorithm: chicken swarm optimization. In International Conference in Swarm Intelligence, 86\u201394. Springer International Publishing, 2014. \n                    https:\/\/doi.org\/10.1007\/978-3-319-11857-4_10\n                    \n                  .","DOI":"10.1007\/978-3-319-11857-4_10"},{"issue":"3","key":"1135_CR32","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh, L. A., Fuzzy sets. Inf. Control. 8(3):338\u2013353, 1965.","journal-title":"Inf. Control."},{"key":"1135_CR33","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1109\/LSP.2014.2364612","volume":"22","author":"YY Deng","year":"2015","unstructured":"Deng, Y. Y., and Dai, Q., Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Processing Letters. 22:573\u2013577, 2015. \n                    https:\/\/doi.org\/10.1109\/LSP.2014.2364612\n                    \n                  .","journal-title":"IEEE Signal Processing Letters."},{"key":"1135_CR34","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.mri.2006.09.043","volume":"25","author":"MS Yang","year":"2007","unstructured":"Yang, M. S., Lin, K. C. R., Liu, H. C., and Lirng, J. F., Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms. Magn. Reson. Imaging 25:265\u2013277. Elsevier, 2007. \n                    https:\/\/doi.org\/10.1016\/j.mri.2006.09.043\n                    \n                  .","journal-title":"Magn. Reson. Imaging"},{"issue":"5","key":"1135_CR35","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/TNN.2007.891635","volume":"18","author":"T Song","year":"2007","unstructured":"Song, T., Jamshidi, M. M., Lee, R. R., and Huang, M., A Modified Probabilistic Neural Network for Partial Volume Segmentation in Brain MR Image. IEEE Trans. on Neural Networks. 18(5):1424\u20131432, 2007. \n                    https:\/\/doi.org\/10.1109\/TNN.2007.891635\n                    \n                  .","journal-title":"IEEE Trans. on Neural Networks."},{"issue":"6","key":"1135_CR36","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick, R. M., and Shanmugam, K., Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 3(6):610\u2013621, 1973.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics."}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10916-018-1135-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-1135-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-018-1135-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T19:37:30Z","timestamp":1577907450000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10916-018-1135-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,2]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,2]]}},"alternative-id":["1135"],"URL":"https:\/\/doi.org\/10.1007\/s10916-018-1135-y","relation":{},"ISSN":["0148-5598","1573-689X"],"issn-type":[{"value":"0148-5598","type":"print"},{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,2]]},"assertion":[{"value":"19 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"25"}}