{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T10:34:02Z","timestamp":1742639642895},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T00:00:00Z","timestamp":1515456000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,4]]},"DOI":"10.1007\/s11042-017-5487-y","type":"journal-article","created":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T06:50:50Z","timestamp":1515480650000},"page":"10285-10301","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Geometric mean filter with grayscale morphological method to enhance the RNFL thickness in the SD-OCT images"],"prefix":"10.1007","volume":"77","author":[{"given":"T.","family":"Senthil Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Helen Prabha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,1,9]]},"reference":[{"issue":"6","key":"5487_CR1","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.compmedimag.2014.06.012","volume":"38","author":"N Anantrasirichai","year":"2014","unstructured":"Anantrasirichai N, Nicholson L, Morgan JE, Erchova I, Mortlock K, North RV et al (2014) Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography. Comput Med Imaging Graph 38(6):526\u2013539","journal-title":"Comput Med Imaging Graph"},{"key":"5487_CR2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ajo.2016.11.001","volume":"174","author":"R Asaoka","year":"2017","unstructured":"Asaoka R, Hirasawa K, Iwase A, Fujino Y, Murata H, Shoji N, Araie M (2017) Validating the usefulness of the \u201crandom forests\u201d classifier to diagnose early glaucoma with optical coherence tomography. Am J Ophthalmol 174:95\u2013103","journal-title":"Am J Ophthalmol"},{"issue":"3","key":"5487_CR3","doi-asserted-by":"crossref","first-page":"e229","DOI":"10.1097\/IJG.0000000000000280","volume":"25","author":"SA Banegas","year":"2016","unstructured":"Banegas SA, Ant\u00f3n A, Morilla A, Bogado M, Ayala EM, Fernandez-Guardiola A, Moreno-Monta\u00f1es J (2016) Evaluation of the retinal nerve fiber layer thickness, the mean deviation, and the visual field index in progressive glaucoma. J Glaucoma 25(3):e229\u2013e235","journal-title":"J Glaucoma"},{"key":"5487_CR4","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.jneumeth.2016.06.014","volume":"270","author":"U Baran","year":"2016","unstructured":"Baran U, Zhu W, Choi WJ, Omori M, Zhang W, Alkayed NJ, Wang RK (2016) Automated segmentation and enhancement of optical coherence tomography-acquired images of rodent brain. J Neurosci Methods 270:132\u2013137","journal-title":"J Neurosci Methods"},{"key":"5487_CR5","doi-asserted-by":"publisher","unstructured":"Birkeldh U, Zahavi O, Manouchehrinia A, Hietala A, Hillert J, Wahlberg-Ramsay M, ... Nilsson M (2017) Retinal nerve fibre layer thickness associates with cognitive impairment and physical disability in multiple sclerosis. Acta Ophthalmol 95(S259). https:\/\/doi.org\/10.1111\/j.1755-3768.2017.0T027","DOI":"10.1111\/j.1755-3768.2017.0T027"},{"key":"5487_CR6","unstructured":"Chen CL, Zhang A, Bojikian KD, Wen JC, Zhang Q, Xin C, ... Wang RK (2016) Peripapillary retinal nerve fiber layer vascular microcirculation in glaucoma using optical coherence tomography\u2013based MicroangiographyVascular microcirculation in RNFL using OMAG. Invest Ophthalmol Vis Sci 57(9):OCT475\u2013OCT485"},{"key":"5487_CR7","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.compmedimag.2016.07.006","volume":"55","author":"Z Gao","year":"2017","unstructured":"Gao Z, Bu W, Zheng Y, Wu X (2017) Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach. Comput Med Imaging Graph 55:42\u201353","journal-title":"Comput Med Imaging Graph"},{"issue":"7","key":"5487_CR8","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1097\/IJG.0000000000000683","volume":"26","author":"JP Goh","year":"2017","unstructured":"Goh JP, Koh V, Chan YH, Ngo C (2017) Macular ganglion cell and retinal nerve fiber layer thickness in children with refractive errors\u2014an optical coherence tomography study. J Glaucoma 26(7):619\u2013625","journal-title":"J Glaucoma"},{"issue":"2","key":"5487_CR9","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1364\/BOE.8.000579","volume":"8","author":"SPK Karri","year":"2017","unstructured":"Karri SPK, Chakraborty D, Chatterjee J (2017) Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed Opt Express 8(2):579\u2013592","journal-title":"Biomed Opt Express"},{"issue":"4","key":"5487_CR10","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s40846-016-0152-x","volume":"36","author":"R Kromer","year":"2016","unstructured":"Kromer R, Shafin R, Boelefahr S, Klemm M (2016) An automated approach for localizing retinal blood vessels in confocal scanning laser ophthalmoscopy fundus images. Journal of Medical and Biological Engineering 36(4):485\u2013494","journal-title":"Journal of Medical and Biological Engineering"},{"key":"5487_CR11","doi-asserted-by":"crossref","unstructured":"Kulcs\u00e1r C, Fezzani R, Le Besnerais G, Plyer A, Levecq X (2014) Fast and robust image registration with local motion estimation for image enhancement and activity detection in retinal imaging. In Computational Intelligence for Multimedia Understanding (IWCIM), 2014 International Workshop on (pp. 1\u20135). IEEE","DOI":"10.1109\/IWCIM.2014.7008811"},{"key":"5487_CR12","doi-asserted-by":"publisher","unstructured":"Kumar PM, Gandhi U, Varatharajan R, Manogaran G, Jidhesh R, Vadivel T (2017) Intelligent face recognition and navigation system using neural learning for smart security in internet of things. Clust Comput, 1\u201312. https:\/\/doi.org\/10.1007\/s10586-017-1323-4","DOI":"10.1007\/s10586-017-1323-4"},{"key":"5487_CR13","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/j.media.2016.08.012","volume":"35","author":"S Lee","year":"2017","unstructured":"Lee S, Charon N, Charlier B, Popuri K, Lebed E, Sarunic MV et al (2017) Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework. Med Image Anal 35:570\u2013581","journal-title":"Med Image Anal"},{"key":"5487_CR14","doi-asserted-by":"crossref","unstructured":"Lopez D, Gunasekaran M (2015) Assessment of vaccination strategies using fuzzy multi-criteria decision making. In Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO-2015). Springer USA, Hyderabad, India pp. 195\u2013208","DOI":"10.1007\/978-3-319-27212-2_16"},{"key":"5487_CR15","unstructured":"Lopez D, Manogaran G (2016) Big data architecture for climate change and disease dynamics. In: Geetam S. Tomar et al. (eds) The human element of big data: issues, analytics, and performance. CRC Press, USA, p 243\u2013265"},{"key":"5487_CR16","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ijid.2016.02.084","volume":"45","author":"D Lopez","year":"2016","unstructured":"Lopez D, Sekaran G (2016) Climate change and disease dynamics-a big data perspective. Int J Infect Dis 45:23\u201324","journal-title":"Int J Infect Dis"},{"key":"5487_CR17","unstructured":"Lopez D, Manogaran G (2017) Modelling the H1N1 influenza using mathematical and neural network approaches. Biomed Res, 28(8): 3212\u20133221"},{"key":"5487_CR18","doi-asserted-by":"crossref","unstructured":"Lopez D, Manogaran G (2017) Parametric model to predict H1N1 influenza in Vellore District, Tamil Nadu, India. In: S Pyne et al. (eds) Handbook of Statistics, Vol 37. Elsevier, USA, pp. 301\u2013316","DOI":"10.1016\/bs.host.2017.09.005"},{"key":"5487_CR19","doi-asserted-by":"crossref","unstructured":"Lopez D, Gunasekaran M, Murugan BS, Kaur H, Abbas KM (2014) Spatial big data analytics of influenza epidemic in Vellore, India. In Big Data (Big Data), 2014 I.E. International Conference on (pp. 19\u201324). IEEE","DOI":"10.1109\/BigData.2014.7004422"},{"issue":"2","key":"5487_CR20","doi-asserted-by":"crossref","first-page":"88","DOI":"10.4018\/IJACI.2017040106","volume":"8","author":"G Manogaran","year":"2017","unstructured":"Manogaran G, Lopez D (2017) Disease surveillance system for big climate data processing and dengue transmission. International Journal of Ambient Computing and Intelligence (IJACI) 8(2):88\u2013105","journal-title":"International Journal of Ambient Computing and Intelligence (IJACI)"},{"key":"5487_CR21","doi-asserted-by":"publisher","unstructured":"Manogaran G, Lopez D (2017) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng. https:\/\/doi.org\/10.1016\/j.compeleceng.2017.04.006","DOI":"10.1016\/j.compeleceng.2017.04.006"},{"key":"5487_CR22","doi-asserted-by":"publisher","unstructured":"Manogaran G, Lopez D (2017) A Gaussian process based big data processing framework in cluster computing environment. Clust Comput 1\u201316. https:\/\/doi.org\/10.1007\/s10586-017-0982-5","DOI":"10.1007\/s10586-017-0982-5"},{"key":"5487_CR23","doi-asserted-by":"crossref","unstructured":"Manogaran, G, Lopez D (2017) A survey of big data architectures and machine learning algorithms in healthcare. Int J Biomed Eng and Technol 25(2\u20134):182\u2013211","DOI":"10.1504\/IJBET.2017.087722"},{"key":"5487_CR24","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.procs.2016.05.138","volume":"87","author":"G Manogaran","year":"2016","unstructured":"Manogaran G, Thota C, Kumar MV (2016) MetaCloudDataStorage architecture for big data security in cloud computing. Procedia Computer Science 87:128\u2013133","journal-title":"Procedia Computer Science"},{"key":"5487_CR25","doi-asserted-by":"publisher","unstructured":"Manogaran, G., Varatharajan, R. & Priyan, M.K. (2017) (Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System). Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-017-5515-y","DOI":"10.1007\/s11042-017-5515-y"},{"key":"5487_CR26","doi-asserted-by":"publisher","unstructured":"Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2017) A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting. Futur Gener Comput Syst. https:\/\/doi.org\/10.1016\/j.future.2017.10.045","DOI":"10.1016\/j.future.2017.10.045"},{"key":"5487_CR27","doi-asserted-by":"publisher","unstructured":"Manogaran G, Vijayakumar V, Varatharajan R, Kumar PM, Sundarasekar R, Hsu CH (2017) Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wirel Pers Commun:1\u201318. https:\/\/doi.org\/10.1007\/s11277-017-5044-z","DOI":"10.1007\/s11277-017-5044-z"},{"key":"5487_CR28","doi-asserted-by":"crossref","unstructured":"Manogaran G, Thota C, Lopez D, Vijayakumar V, Abbas KM, Sundarsekar R (2017a) Big data knowledge system in healthcare. In: Chinthan Butt et al. (eds) Internet of things and big data technologies for next generation healthcare. Springer International Publishing, USA, pp. 133\u2013157","DOI":"10.1007\/978-3-319-49736-5_7"},{"key":"5487_CR29","doi-asserted-by":"crossref","unstructured":"Manogaran G, Thota C, Lopez D, Sundarsekar R, Abbas KM (2017b) \u201cBig data analytics in healthcare internet of things\u201d, In: Qudrat-Ullah H (ed) Innovative Health Systems for the 21st Century. Springer International Publishing, USA, pp.123\u2013137","DOI":"10.1007\/978-3-319-55774-8_10"},{"key":"5487_CR30","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.ajo.2015.09.019","volume":"161","author":"JC Mwanza","year":"2016","unstructured":"Mwanza JC, Lee G, Budenz DL (2016) Effect of adjusting retinal nerve fiber layer profile to fovea-disc angle Axis on the thickness and glaucoma diagnostic performance. Am J Ophthalmol 161:12\u201321","journal-title":"Am J Ophthalmol"},{"key":"5487_CR31","doi-asserted-by":"crossref","unstructured":"Rao HL, Venkatesh CR, Vidyasagar K, Yadav RK, Addepalli UK, Jude A, ... Garudadri CS (2014) Retinal nerve fiber layer measurements by scanning laser polarimetry with enhanced corneal compensation in healthy subjects. J Glaucoma, 23(9):589\u2013593","DOI":"10.1097\/IJG.0b013e318286ffa5"},{"issue":"1","key":"5487_CR32","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4103\/2228-7477.150414","volume":"5","author":"SH Rasta","year":"2015","unstructured":"Rasta SH, Partovi ME, Seyedarabi H, Javadzadeh A (2015) A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement. Journal of Medical Signals and Sensors 5(1):40","journal-title":"Journal of Medical Signals and Sensors"},{"key":"5487_CR33","doi-asserted-by":"crossref","unstructured":"Sudeep PV, Niwas SI, Palanisamy P, Rajan J, Xiaojun Y, Wang X, ... Liu L (2016). Enhancement and bias removal of optical coherence tomography images: an iterative approach with adaptive bilateral filtering. Comput Biol Med 71:97\u2013107","DOI":"10.1016\/j.compbiomed.2016.02.003"},{"key":"5487_CR34","unstructured":"The Hindu (2017) Retrieved 27 January 2017, from 12. http:\/\/www.thehindu.com\/news\/cities\/Hyderabad\/many-unaware-of-alzheimers-disease-in-india\/article5390719.ece"},{"key":"5487_CR35","doi-asserted-by":"crossref","unstructured":"Thota C, Sundarasekar R, Manogaran G, Varatharajan R, Priyan MK (2018) Centralized fog computing security platform for IoT and cloud in healthcare system. In: H Q et al (eds) Exploring the convergence of big data and the internet of things. IGI Global, USA, pp. 141\u2013154","DOI":"10.4018\/978-1-5225-2947-7.ch011"},{"key":"5487_CR36","doi-asserted-by":"crossref","unstructured":"Uji A, Murakami T, Muraoka Y, Hosoda Y, Yoshitake S, Dodo Y, ... Yoshimura N (2015) Potential measurement errors due to image enlargement in optical coherence tomography imaging. PLoS One, 10(5):e0128512","DOI":"10.1371\/journal.pone.0128512"},{"key":"5487_CR37","doi-asserted-by":"publisher","unstructured":"Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2017) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust Comput 1\u201310. https:\/\/doi.org\/10.1007\/s11227-017-2169-5","DOI":"10.1007\/s11227-017-2169-5"},{"key":"5487_CR38","doi-asserted-by":"publisher","unstructured":"Varatharajan R, Manogaran G, Priyan MK, Bala\u015f VE, Barna C (2017) Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimed Tools Appl 1\u201321. https:\/\/doi.org\/10.1007\/s11042-017-4768-9","DOI":"10.1007\/s11042-017-4768-9"},{"key":"5487_CR39","doi-asserted-by":"publisher","unstructured":"Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao XZ (2017) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput Electr Eng. https:\/\/doi.org\/10.1016\/j.compeleceng.2017.05.035","DOI":"10.1016\/j.compeleceng.2017.05.035"},{"key":"5487_CR40","doi-asserted-by":"publisher","unstructured":"Varatharajan R, Manogaran G, Priyan MK (2017) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-017-5318-1","DOI":"10.1007\/s11042-017-5318-1"},{"issue":"1","key":"5487_CR41","doi-asserted-by":"crossref","first-page":"e0170341","DOI":"10.1371\/journal.pone.0170341","volume":"12","author":"HS Yang","year":"2017","unstructured":"Yang HS, Woo JE, Kim MH, Kim DY, Yoon YH (2017) Co-evaluation of Peripapillary RNFL thickness and retinal thickness in patients with diabetic macular edema: RNFL misinterpretation and its adjustment. PLoS One 12(1):e0170341","journal-title":"PLoS One"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-017-5487-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5487-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5487-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T00:39:47Z","timestamp":1570581587000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-017-5487-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,9]]},"references-count":41,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2018,4]]}},"alternative-id":["5487"],"URL":"https:\/\/doi.org\/10.1007\/s11042-017-5487-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,9]]}}}