{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T12:43:19Z","timestamp":1769949799811,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T00:00:00Z","timestamp":1552608000000},"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":[[2019,5]]},"DOI":"10.1007\/s10916-019-1227-3","type":"journal-article","created":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T20:15:08Z","timestamp":1552680908000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9682-1772","authenticated-orcid":false,"given":"C.","family":"Priya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Sudha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,15]]},"reference":[{"issue":"10","key":"1227_CR1","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1016\/j.echo.2011.06.013","volume":"24","author":"R Sicari","year":"2011","unstructured":"Sicari, R., Sironi, A. M., Petz, R., Frassi, F., Chubuchny, V., De Marchi, D., Positano, V., Lombardi, M., Picano, E., and Gastaldelli, A., Pericardial rather than epicardial fat is a cardiometabolic risk marker: An MRI vs echo study. J. Am. Soc. Echocardiogr. 24(10):1156\u20131162, 2011.","journal-title":"J. Am. Soc. Echocardiogr."},{"issue":"6","key":"1227_CR2","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/j.ahj.2007.03.019","volume":"153","author":"HS Sacks","year":"2007","unstructured":"Sacks, H. S., and Fain, J. N., Human epicardial adipose tissue: A review. Am. Heart J. 153(6):907\u2013917, 2007.","journal-title":"Am. Heart J."},{"key":"1227_CR3","doi-asserted-by":"crossref","unstructured":"Ding, X., Terzopoulos, D., Diaz-Zamudio, M., Berman, D.S., Slomka P.J., and Dey, D., Automated epicardial fat volume quantification from non-contrast CT. J. Med. Imag., 90340I-9034, 2014.","DOI":"10.1117\/12.2043326"},{"key":"1227_CR4","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.ijcard.2014.10.030","volume":"179","author":"G Pontone","year":"2015","unstructured":"Pontone, G., Andreini, D., Bertella, E., Petull\u00e0, M., Russo, E., Innocenti, E., and Mushtaq, S., Comparison of cardiac computed tomography versus cardiac magnetic resonance for characterization of left atrium anatomy before radiofrequency catheter ablation of atrial fibrillation. Int. J. Cardiol. 179:114\u2013112, 2015.","journal-title":"Int. J. Cardiol."},{"issue":"2","key":"1227_CR5","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.jcct.2015.10.006","volume":"10","author":"S Oda","year":"2016","unstructured":"Oda, S., Utsunomiya, D., Funama, Y., Yuki, H., Kidoh, M., Nakaura, T., and Takaoka, H., Effect of iterative reconstruction on variability and reproducibility of epicardial fat volume quantification by cardiac CT. J. Cardiovasc. Comput. Tomograp. 10(2):150\u2013155, 2016.","journal-title":"J. Cardiovasc. Comput. Tomograp."},{"issue":"1","key":"1227_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.jcct.2013.01.002","volume":"7","author":"M Marwan","year":"2013","unstructured":"Marwan, M., and Achenbach, S., Quantification of epicardial fat by computed tomography: Why, when and how? J. Cardiovasc. Comput. Tomograp. 7(1):3\u201310, 2013.","journal-title":"J. Cardiovasc. Comput. Tomograp."},{"issue":"3","key":"1227_CR7","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.amjcard.2013.10.014","volume":"113","author":"P Apfaltrer","year":"2015","unstructured":"Apfaltrer, P., Schindler, A., Schoepf, U. J., Nance, J. W., Tricarico, F., Ebersberger, U., and McQuiston, A. D., Comparison of epicardial fat volume by computed tomography in black versus white patients with acute chest pain. Am. J. Cardiol. 113(3):422\u2013428, 2015.","journal-title":"Am. J. Cardiol."},{"issue":"4","key":"1227_CR8","doi-asserted-by":"publisher","first-page":"235","DOI":"10.15171\/jcvtr.2014.018","volume":"6","author":"N Aslanabadi","year":"2014","unstructured":"Aslanabadi, N., Salehi, R., Javadrashid, A., Tarzamni, M., Khodadad, B., Enamzadeh, E., and Montazerghaem, H., Epicardial and pericardial fat volume correlate with the severity of coronary artery stenosis. J. Cardiovasc. Thorac. Res. 6(4):235\u2013239, 2014.","journal-title":"J. Cardiovasc. Thorac. Res."},{"key":"1227_CR9","unstructured":"Yalamanchilia, R., Dey, D., Kurkure, U., Nakazato, R., Berman, D. S., and Kakadiaris, I. A., Knowledge-based quantification of pericardial fat in non-contrast CT data. Proc. SPIE 7623:76231X\u2013762317X."},{"key":"1227_CR10","unstructured":"Rodrigues, \u00c9. O., Conci, A., Morais, F. F. C., and P\u00e9rez, M. G., Towards the automated segmentation of epicardial and mediastinal fats. 2015 Elsevier Ireland Ltd. All rights reserved."},{"issue":"6","key":"1227_CR11","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.compmedimag.2005.04.001","volume":"29","author":"D-T Lin","year":"2005","unstructured":"Lin, D.-T., Yan, C.-R., and Chen, W.-T., Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system. J. Comput. Med. Imag. Graph. 29(6):447\u2013458, 2005.","journal-title":"J. Comput. Med. Imag. Graph."},{"issue":"2","key":"1227_CR12","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1016\/j.hrthm.2015.01.015","volume":"12","author":"Z Ling","year":"2015","unstructured":"Ling, Z., McManigle, J., Zipunnikov, V., Pashakhanloo, F., Khurram, I. M., Zimmerman, S. L., and Philips, B., The association of left atrial low-voltage regions on electro anatomic mapping with low attenuation regions on cardiac computed tomography perfusion imaging in patients with atrial fibrillation. J. Heart Rhythm 12(2):857\u2013864, 2015.","journal-title":"J. Heart Rhythm"},{"issue":"7","key":"1227_CR13","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1109\/TMI.2008.2011480","volume":"28","author":"I Isgum","year":"2009","unstructured":"Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M. A., and Van Ginneken, B., Multi-atlas-based segmentation with local decision fusion\u2014Application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28(7):1000\u20131010, 2009.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1227_CR14","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.cmpb.2015.09.017","volume":"123","author":"\u00c9.O. Rodrigues","year":"2016","unstructured":"Rodrigues, \u00c9. O., Morais, F. F. C., Morais, N. A. O. S., Conci, L. S., Neto, L. V., and Conci, A., A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography. Comput. Methods Prog. Biomed. 123:109\u2013128, 2016.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"1227_CR15","first-page":"31","volume":"20","author":"J Champier","year":"2006","unstructured":"Champier, J., Cinotti, L., Bordet, J.-C., Lavenne, F., and Mallet, J.-J., Delineation and quantization of brain lesions by fuzzy clustering in positron emission tomography. J. Comput. Med. Imaging Graph. 20(1):31\u201341, 2006.","journal-title":"J. Comput. Med. Imaging Graph."},{"key":"1227_CR16","doi-asserted-by":"crossref","unstructured":"Bandekar, A. N., Naghavi, M. and Kakadiaris, I. A., Automated pericardial fat quantification in CT data. Proceedings of the 28th IEEE EMBS annual international conference new York City, USA, 2006.","DOI":"10.1109\/IEMBS.2006.259259"},{"key":"1227_CR17","unstructured":"Guo, Z., Xin, Y., Liu, S., Lv, X. and Li, S., Comparisons of fat quantification methods based on MRI segmentation. IEEE, 978\u20131\u20134799-3979-4\/14\/$31.00, 2014."},{"key":"1227_CR18","doi-asserted-by":"crossref","unstructured":"Zlokolica, V., Velicki, L., Janev, M., Mitrinovic, D., and Babin, D., Epicardial fat registration by local adaptive morphology-thresholding based 2D segmentation. IEEE, 2014.","DOI":"10.1109\/ELMAR.2014.6923347"},{"key":"1227_CR19","unstructured":"Sumitpaibul, P., Damrongphithakkul, A., and Watchareeruetai, U., Fat detection algorithm for liver biopsy images. IEEE, 978\u20131\u20134799-3174-3\/14\/$31.00, 2014."},{"key":"1227_CR20","unstructured":"Antony, J., McGuinness, K., Welch, N., Coyle, J., and Franklyn-Miller, A., Fat quantification in MRI-defined lumbar muscles. IEEE, 978\u20131\u20134799-6463-5\/14\/$31.00, 2014."},{"key":"1227_CR21","unstructured":"Yan, Z., Tan, C., Zhang, S., Zhou, Y., and Belaroussi, B., Automatic liver segmentation and hepatic fat fraction assessment in MRI. IEEE, 1051\u20134651\/14 $31.00, 2014."},{"key":"1227_CR22","unstructured":"Ahmed, S., Gilles, B., Puech, W., Hassouni, M. E and Rziza, M., Hierarchical MRI segmentation of the musculoskeletal system using texture analysis and topological constraints. 978\u20131\u20134799-4572-6\/14\/$31.00, 2014."},{"key":"1227_CR23","unstructured":"Song, H., Zhang, Q., Sun, F., Wang, J., and Wang, Q., Breast tissue segmentation on MR images using KFCM with spatial constraints. IEEE, 978\u20131\u20134799-5464-3\/14\/$31.00, 2014."},{"key":"1227_CR24","unstructured":"Ahmad, E., Yap, M. H., Degens, H. and McPhee, J., Enhancement of MRI human thigh muscle segmentation by template-based framework. IEEE, 978\u20131\u20134799-5686-9\/14\/$31.00, 2014."},{"key":"1227_CR25","unstructured":"Spasojevi\u00fc, A., Stojanov, O., Turukalo, T. L., and \u0160veljo, O., Estimation of subcutaneous and visceral fat tissue volume on abdominal MR images. IEEE, 978\u20131\u20134799-5888-7\/14\/$31.00, 2014."},{"key":"1227_CR26","doi-asserted-by":"crossref","unstructured":"Ayerdi, B., Echaniz, O., Savio, A., and Gra\u00f1a, M., Automated segmentation of subcutaneous and visceral adipose tissues from MRI. Springer. 427\u2013433, 2016.","DOI":"10.1007\/978-3-319-23024-5_39"},{"issue":"5","key":"1227_CR27","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.numecd.2009.10.010","volume":"21","author":"T.E. Brinkley","year":"2011","unstructured":"Brinkley, T. E., Tina, E., Hsu, F.-C., Jeffrey Carr, J., Gregory Hundley, W., Bluemke, D. A., Polak, J. F., and Ding, J., Pericardial fat is associated with carotid stiffness in the multi-ethnic study of atherosclerosis. J. Nutr. Metab. Cardiovasc. Dis. 21(5):332\u2013338, 2011.","journal-title":"Nutrition, Metabolism and Cardiovascular Diseases"},{"key":"1227_CR28","doi-asserted-by":"crossref","unstructured":"Bandekar, A. N., Naghavi, M., and Kakadiaris, I. A., Automated pericardial fat quantification in CT data. Eng. Med. Biol. Soc. Int. Conf. IEEE. 932\u2013935, IEEE, 2006.","DOI":"10.1109\/IEMBS.2006.259259"},{"issue":"3","key":"1227_CR29","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.media.2004.06.015","volume":"8","author":"M KAUS","year":"2004","unstructured":"Kaus, M. R., von Berg, J., Weese, J., Niessen, W., and Pekar, V., Automated segmentation of the left ventricle in cardiac MRI. J. Med. Image Anal. 8(3):245\u2013254, 2004.","journal-title":"Medical Image Analysis"},{"key":"1227_CR30","doi-asserted-by":"publisher","unstructured":"Premkumar, R., and Anand, S., Secured and compound 3-D chaos image encryption using hybrid mutation and crossover operator. Multimed. Tools Applic., 2018. \n                    https:\/\/doi.org\/10.1007\/s11042-018-6534-z\n                    \n                  .","DOI":"10.1007\/s11042-018-6534-z"},{"key":"1227_CR31","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.atherosclerosis.2016.09.010","volume":"254","author":"LC Mangili","year":"2016","unstructured":"Mangili, L. C., Mangili, O. C., Bittencourt, M. S., Miname, M. H., Harada, P. H., Lima, L. M., Rochitte, C. E., and Santos, R. D., Epicardial fat is associated with severity of subclinical coronary atherosclerosis in familial hypercholesterolemia. J. Atherosclero. 254:73\u201377, 2016.","journal-title":"J. Atherosclero."},{"key":"1227_CR32","doi-asserted-by":"crossref","unstructured":"Meenakshi, K., Rajendran, M., Srikumar, S., Chidambaram, S., Epicardial fat thickness: A surrogate marker of coronary artery disease \u2013 Assessment by echocardiography. 2015 cardio logical Society of India, published by Elsevier B. V, All rights reserved.","DOI":"10.1016\/j.ihj.2015.08.005"},{"issue":"10","key":"1227_CR33","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1016\/j.echo.2011.06.013","volume":"24","author":"Rosa Sicari","year":"2011","unstructured":"Sicari, R., Sironi, A. M., Petz, R., Frassi, F., Chubuchny, V., Marchi, D.D., Positano, V., Lombardi, M., Picano, E., and Gastaldelli, A., Pericardial rather than Epicardial fat is a Cardiometabolic risk marker: An MRI vs Echo study. J. Am. Soc. Echocardiogr., 24(10).","journal-title":"Journal of the American Society of Echocardiography"},{"key":"1227_CR34","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.ins.2014.10.051","volume":"316","author":"F Yang","year":"2015","unstructured":"Yang, F., Ding, M., Zhang, X., Hou, W., and Zhong, C., Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarm optimization. J. Inf. Sci. 316:440\u2013456, 2015.","journal-title":"J. Inf. Sci."},{"key":"1227_CR35","unstructured":"Guo, Z., Xin, Y., Liu, S., Lv, X. and Li, S., Comparisons of fat quantification methods based on MRI segmentation. IEEE, 978\u20131\u20134799-3979-4\/14\/$31.00, 2014."},{"issue":"6","key":"1227_CR36","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1016\/j.ejrad.2015.03.018","volume":"84","author":"AM Bucher","year":"2015","unstructured":"Bucher, A. M., Joseph Schoepf, U., Krazinski, A. W., Silverman, J., Spearman, J. V., De Cecco, C. N., Meinel, F. G., Vogl, T. J., and Geyer, L. L., Influence of technical parameters on epicardial fat volume quantification at cardiac CT. Eur. J. Radiol. 84(6):1062\u20131067, 2015.","journal-title":"Eur. J. Radiol."},{"issue":"8","key":"1227_CR37","doi-asserted-by":"publisher","first-page":"2012","DOI":"10.1166\/jmihi.2016.1966","volume":"6","author":"R Premkumar","year":"2016","unstructured":"Premkumar, R., and Anand, S., Secured permutation and substitution based image encryption algorithm for medical security application. J. Med. Imag. Health Inform. 6(8):2012\u20132018, 2016.","journal-title":"J. Med. Imag. Health Inform."}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-019-1227-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10916-019-1227-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-019-1227-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:11:01Z","timestamp":1584144661000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10916-019-1227-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,15]]},"references-count":37,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,5]]}},"alternative-id":["1227"],"URL":"https:\/\/doi.org\/10.1007\/s10916-019-1227-3","relation":{},"ISSN":["0148-5598","1573-689X"],"issn-type":[{"value":"0148-5598","type":"print"},{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,15]]},"assertion":[{"value":"9 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 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":"Ethical Approval"}}],"article-number":"104"}}