{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T06:17:23Z","timestamp":1774160243107,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T00:00:00Z","timestamp":1568592000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T00:00:00Z","timestamp":1568592000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s10278-019-00276-2","type":"journal-article","created":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T19:02:31Z","timestamp":1568660551000},"page":"465-479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Three-Phase Automatic Brain Tumor Diagnosis System Using Patches Based Updated Run Length Region Growing Technique"],"prefix":"10.1007","volume":"33","author":[{"given":"T.","family":"Kalaiselvi","sequence":"first","affiliation":[]},{"given":"P.","family":"Kumarashankar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5339-673X","authenticated-orcid":false,"given":"P.","family":"Sriramakrishnan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,16]]},"reference":[{"key":"276_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.imu.2016.01.002","volume":"1","author":"JA Rodger","year":"2015","unstructured":"Rodger JA: Discovery of medical big data analytics: Improving the prediction of traumatic brain injury survival rates by data mining patient informatics processing software hybrid Hadoop hive. Informatics in Medicine Unlocked 1:17\u201326, 2015","journal-title":"Informatics in Medicine Unlocked"},{"issue":"3","key":"276_CR2","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1002\/ima.22267","volume":"28","author":"T Kalaiselvi","year":"2018","unstructured":"Kalaiselvi T, Sriramakrishnan P: Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine. International Journal of Imaging Systems and Technology 28(3):163\u2013174, 2018","journal-title":"International Journal of Imaging Systems and Technology"},{"issue":"3","key":"276_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0033182","volume":"7","author":"D Zhang","year":"2012","unstructured":"Zhang D, Shen D, Alzheimer's Disease Neuroimaging Initiative: Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS one 7(3):e33182, 2012","journal-title":"PloS one"},{"issue":"1","key":"276_CR4","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s40708-017-0075-5","volume":"5","author":"NV Shree","year":"2018","unstructured":"Shree NV, Kumar TNR: Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain informatics 5(1):23\u201330, 2018","journal-title":"Brain informatics"},{"key":"276_CR5","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.procs.2018.01.117","volume":"127","author":"M Khalil","year":"2018","unstructured":"Khalil M, Ayad H, Adib A: Performance evaluation of feature extraction techniques in MR-brain image classification system. Procedia Computer Science 127:218\u2013225, 2018","journal-title":"Procedia Computer Science"},{"key":"276_CR6","doi-asserted-by":"publisher","unstructured":"Thillaikkarasi R, Saravanan S: An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM. J Med Syst 43(84), 2019. https:\/\/doi.org\/10.1007\/s10916-019-1223-7","DOI":"10.1007\/s10916-019-1223-7"},{"key":"276_CR7","unstructured":"Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2017). Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In International MICCAI Brainlesion Workshop (pp. 178\u2013190). Springer, Cham."},{"issue":"6","key":"276_CR8","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, Pan Y: A survey of MRI-based brain tumor segmentation methods. Tsinghua Science and Technology 19(6):578\u2013595, 2014","journal-title":"Tsinghua Science and Technology"},{"issue":"2","key":"276_CR9","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s11760-013-0456-z","volume":"9","author":"MP Arakeri","year":"2015","unstructured":"Arakeri MP, Reddy GRM: Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images. Signal, Image and Video Processing 9(2):409\u2013425, 2015","journal-title":"Signal, Image and Video Processing"},{"issue":"1","key":"276_CR10","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/s13640-018-0332-4","volume":"2018","author":"MK Abd-Ellah","year":"2018","unstructured":"Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing 2018(1):97, 2018","journal-title":"EURASIP Journal on Image and Video Processing"},{"issue":"2","key":"276_CR11","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1002\/ima.22215","volume":"27","author":"P Sivakumar","year":"2017","unstructured":"Sivakumar P, Ganeshkumar P: CANFIS based glioma brain tumor classification and retrieval system for tumor diagnosis. International Journal of Imaging Systems and Technology 27(2):109\u2013117, 2017","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"276_CR12","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2015.11.034","volume":"177","author":"DR Nayak","year":"2016","unstructured":"Nayak DR, Dash R, Majhi B: Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188\u2013197, 2016","journal-title":"Neurocomputing"},{"key":"276_CR13","unstructured":"Qurat-Ul-Ain, G. L., Kazmi, S. B., Jaffar, M. A., & Mirza, A. M. (2010). Classification and segmentation of brain tumor using texture analysis. Recent advances in artificial intelligence, knowledge engineering and data bases, 147\u2013155."},{"key":"276_CR14","unstructured":"Chen, L., Wu, Y., DSouza, A. M., Abidin, A. Z., Wism\u00fcller, A., & Xu, C. (2018). MRI tumor segmentation with densely connected 3D CNN. In Medical Imaging 2018: Image Processing (Vol. 10574, p. 105741F) International Society for Optics and Photonics."},{"key":"276_CR15","unstructured":"Chugh S, Anand SM: Pixel run length based adaptive region growing (PRL-ARG) technique for segmentation of tumor from MRI images. In: International conference on computer and electrical engineering 4th (ICCEE 2011). ASME Press, 2011"},{"issue":"10","key":"276_CR16","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1016\/j.compbiomed.2010.08.004","volume":"40","author":"K Somasundaram","year":"2010","unstructured":"Somasundaram K, Kalaiselvi T: Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images. Computers in biology and medicine 40(10):811\u2013822, 2010","journal-title":"Computers in biology and medicine"},{"key":"276_CR17","doi-asserted-by":"crossref","unstructured":"Roffo, G., Melzi, S., & Cristani, M. (2015). Infinite feature selection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4202\u20134210).","DOI":"10.1109\/ICCV.2015.478"},{"issue":"3","key":"276_CR18","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V: Support-vector networks. Machine learning 20(3):273\u2013297, 1995","journal-title":"Machine learning"},{"issue":"6","key":"276_CR19","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/34.295913","volume":"16","author":"R Adams","year":"1994","unstructured":"Adams R, Bischof L: Seeded region growing. IEEE Transactions on pattern analysis and machine intelligence 16(6):641\u2013647, 1994","journal-title":"IEEE Transactions on pattern analysis and machine intelligence"},{"key":"276_CR20","unstructured":"Gonzalez, R. C. (1992). RE woods digital image processing. Addison\u2013Wesely Publishing Company."},{"issue":"2","key":"276_CR21","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/0165-0270(90)90101-K","volume":"35","author":"GD Rosen","year":"1990","unstructured":"Rosen GD, Harry JD: Brain volume estimation from serial section measurements: a comparison of methodologies. Journal of neuroscience methods 35(2):115\u2013124, 1990","journal-title":"Journal of neuroscience methods"},{"key":"276_CR22","unstructured":"http:\/\/www.med.harvard.edu\/aanlib\/ , Last accessed 14th Aug 2019."},{"key":"276_CR23","unstructured":"https:\/\/www.smir.ch\/BRATS\/Start2013 , Last accessed 14th Aug 2019."},{"issue":"3","key":"276_CR24","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR: Measures of the amount of ecologic association between species. Ecology 26(3):297\u2013302, 1945","journal-title":"Ecology"},{"key":"276_CR25","unstructured":"Bauer, S., Tessier, J., Krieter, O., Nolte, L. P., & Reyes, M. (2013). Integrated spatio-temporal segmentation of longitudinal brain tumor imaging studies. In International MICCAI Workshop on Medical Computer Vision (pp. 74\u201383). Springer, Cham."},{"key":"276_CR26","unstructured":"Buendia P, Taylor T, Ryan M, John N: A grouping artificial immune network for segmentation of tumor images. Multimodal Brain Tumor Segmentation 1, 2013"},{"key":"276_CR27","unstructured":"Cordier, N., Menze, B., Delingette, H., & Ayache, N. (2013). Patch-based segmentation of brain tissues. In MICCAI challenge on multimodal brain tumor segmentation(pp. 6\u201317). IEEE."},{"issue":"4","key":"276_CR28","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1109\/JBHI.2014.2360515","volume":"19","author":"A Demirhan","year":"2015","unstructured":"Demirhan A, T\u00f6r\u00fc M, G\u00fcler I: Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE journal of biomedical and health informatics 19(4):1451\u20131458, 2015","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"276_CR29","unstructured":"Doyle S, Vasseur F, Dojat M, Forbes F: Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM. Procs. NCI-MICCAI BraTS:18\u201322, 2013"},{"key":"276_CR30","doi-asserted-by":"crossref","unstructured":"Pereira, S., Festa, J., Mariz, J. A., Sousa, N., & Silva, C. A. (2013). Automatic brain tissue segmentation of multi-sequence MR images using random decision forests. Proceedings of the MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS\u201913).","DOI":"10.54294\/azta8g"},{"key":"276_CR31","unstructured":"Geremia, E., Menze, B. H., & Ayache, N. (2012). Spatial decision forests for glioma segmentation in multi-channel MR images. MICCAI Challenge on Multimodal Brain Tumor Segmentation, 34."},{"key":"276_CR32","unstructured":"Guo X, Schwartz L, Zhao B: Semi-automatic segmentation of multimodal brain tumor using active contours. Multimodal Brain Tumor Segmentation 27, 2013"},{"issue":"3","key":"276_CR33","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/TMI.2011.2181857","volume":"31","author":"A Hamamci","year":"2012","unstructured":"Hamamci A, Kucuk N, Karaman K, Engin K, Unal G: Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE transactions on medical imaging 31(3):790\u2013804, 2012","journal-title":"IEEE transactions on medical imaging"},{"key":"276_CR34","unstructured":"Meier R, Bauer S, Slotboom J, Wiest R, Reyes M: Appearance-and context-sensitive features for brain tumor segmentation. Proceedings of MICCAI BRATS Challenge:020\u2013026, 2014"},{"key":"276_CR35","doi-asserted-by":"crossref","unstructured":"Reza S, Iftekharuddin KM: Multi-class abnormal brain tissue segmentation using texture. Multimodal Brain Tumor Segmentation 38, 2013","DOI":"10.1364\/QMI.2013.QW2G.2"},{"key":"276_CR36","unstructured":"Raviv, T. R., Leemput, K. V., & Menze, B. H. (2012, October). Multi-modal brain tumor segmentation via latent atlases. In Proceeding MICCAI-BRATS (pp. 64\u201373)."},{"key":"276_CR37","unstructured":"Shin, H. C. (2012). Hybrid clustering and logistic regression for multi-modal brain tumor segmentation. In Proc. of Workshops and Challanges in Medical Image Computing and Computer-Assisted Intervention (MICCAI\u201912)."},{"key":"276_CR38","first-page":"751","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"NK Subbanna","year":"2013","unstructured":"Subbanna NK, Precup D, Collins DL, Arbel T: Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes. In: International conference on medical image computing and computer-assisted intervention. Berlin, Heidelberg: Springer, 2013, pp. 751\u2013758"},{"key":"276_CR39","unstructured":"Taylor T, John N, Buendia P, Ryan M: Map-reduce enabled hidden Markov models for high throughput multimodal brain tumor segmentation. Multimodal Brain Tumor Segmentation:43, 2013"},{"key":"276_CR40","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3389\/fnins.2013.00162","volume":"7","author":"NJ Tustison","year":"2013","unstructured":"Tustison NJ, Johnson HJ, Rohlfing T, Klein A, Ghosh SS, Ibanez L, Avants B: Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences. Frontiers in neuroscience 7:162, 2013","journal-title":"Frontiers in neuroscience"},{"key":"276_CR41","unstructured":"Zhao L, Sarikaya D, Corso JJ: Automatic brain tumor segmentation with MRF on supervoxels. Multimodal Brain Tumor Segmentation 51, 2013"},{"key":"276_CR42","first-page":"369","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"D Zikic","year":"2012","unstructured":"Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: International conference on medical image computing and computer-assisted intervention. Berlin, Heidelberg: Springer, 2012, October, pp. 369\u2013376"},{"issue":"2","key":"276_CR43","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.bbe.2019.02.002","volume":"39","author":"P Sriramakrishnan","year":"2019","unstructured":"Sriramakrishnan P, Kalaiselvi T, Rajeswaran R: Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybernetics and Biomedical Engineering 39(2):470\u2013487, 2019","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"276_CR44","unstructured":"Kalaiselvi, T., Kumarashankar, P., & Sriramakrishnan, (2019). P. Reliability of segmenting brain tumor and finding optimal volume estimator for MR images of patients with glioma\u2019s, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 8, No. 9, pp. 1647\u20131652."}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00276-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-019-00276-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00276-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T21:43:22Z","timestamp":1664401402000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-019-00276-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,16]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["276"],"URL":"https:\/\/doi.org\/10.1007\/s10278-019-00276-2","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,16]]},"assertion":[{"value":"16 September 2019","order":1,"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 interests and the paper has not been submitted elsewhere.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}