{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T09:08:23Z","timestamp":1774084103314,"version":"3.50.1"},"reference-count":30,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>The identification, segmentation, and detection of the infected area in brain tumor is a tedious and a time-consuming task. The different structures of the human body can be visualized by an image processing concept, an MRI. It is very difficult to visualize abnormal structures of the human brain using simple imaging techniques. An MRI technique contains many imaging modalities that scan and capture the internal structure of the human brain. This article concentrates on a noise removal technique, followed by improvement of medical images for a correct diagnosis using a balance contrast enhancement technique (BCET). Then, image segmentation is used. Finally, the Canny edge detection method is applied to detect the fine edges. The experiment results achieved nearly 98% accuracy in detecting the area of the tumor and normal brain regions in MRI images demonstrating the effectiveness of the proposed technique.<\/jats:p>","DOI":"10.4018\/ijapuc.2019010104","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T14:25:32Z","timestamp":1552055132000},"page":"45-60","source":"Crossref","is-referenced-by-count":14,"title":["Detection of Brain Tumor in MRI Images, Using a Combination of Fuzzy C-Means and Thresholding"],"prefix":"10.4018","volume":"11","author":[{"given":"Yousif Ahmed","family":"Hamad","sequence":"first","affiliation":[{"name":"Siberian Federal University, Krasnoyarsk, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantin Vasilievich","family":"Simonov","sequence":"additional","affiliation":[{"name":"Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences, Krasnoyarsk, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2247-427X","authenticated-orcid":true,"given":"Mohammad B.","family":"Naeem","sequence":"additional","affiliation":[{"name":"Al-Maarif University College, Ramadi, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"IJAPUC.2019010104-0","doi-asserted-by":"publisher","DOI":"10.1117\/12.2272792"},{"key":"IJAPUC.2019010104-1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-013-0922-7"},{"key":"IJAPUC.2019010104-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.08.005"},{"key":"IJAPUC.2019010104-3","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.04.004"},{"key":"IJAPUC.2019010104-4","doi-asserted-by":"crossref","unstructured":"Leite, M., Gobbi, D., Salluzi, M., Frayne, R., Lotufo, R., & Rittner, L. (2016, March). 3D texture-based classification applied on brain white matter lesions on MR images. In Medical Imaging 2016: Computer-Aided Diagnosis (p. 97852N). International Society for Optics and Photonics.","DOI":"10.1117\/12.2216285"},{"key":"IJAPUC.2019010104-5","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2007.70602"},{"key":"IJAPUC.2019010104-6","doi-asserted-by":"crossref","unstructured":"Ain, Q., Jaffar, M. A., & Choi, T. S. (2014). Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor. applied soft computing, 21, 330-340.","DOI":"10.1016\/j.asoc.2014.03.019"},{"key":"IJAPUC.2019010104-7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2015.01.003"},{"issue":"16","key":"IJAPUC.2019010104-8","article-title":"Application of edge detection for brain tumor detection.","volume":"58","author":"P.Sharma","year":"2012","journal-title":"International Journal of Computers and Applications"},{"key":"IJAPUC.2019010104-9","first-page":"186","article-title":"Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm.","author":"J.Selvakumar","year":"2012","journal-title":"2012 International Conference on Advances in Engineering, Science and Management (ICAESM)"},{"issue":"3","key":"IJAPUC.2019010104-10","first-page":"16","article-title":"Determination of gray matter (GM) and white matter (WM) volume in brain magnetic resonance images (MRI).","volume":"45","author":"E. A.Zanaty","year":"2012","journal-title":"International Journal of Computers and Applications"},{"key":"IJAPUC.2019010104-11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"IJAPUC.2019010104-12","unstructured":"H\u00e4user, S., & Steidl, G. (2012). Fast finite shearlet transform. arXiv:1202.1773"},{"key":"IJAPUC.2019010104-13","unstructured":"Stosic, Z., & Rutesic, P. (2018). An Improved Canny Edge Detection Algorithm for Detecting Brain Tumors in MRI Images. International Journal of Signal Processing, 3."},{"key":"IJAPUC.2019010104-14","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2006.01.025"},{"key":"IJAPUC.2019010104-15","doi-asserted-by":"publisher","DOI":"10.1007\/11866763_97"},{"key":"IJAPUC.2019010104-16","unstructured":"Verma, V. S. (2017, February). New morphological technique for medical image segmentation. In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 1-5). IEEE."},{"issue":"1","key":"IJAPUC.2019010104-17","first-page":"1","article-title":"Brain tumour extraction from MRI images using MATLAB.","volume":"2","author":"C. P.Rajesh","year":"2012","journal-title":"Int. J. Electron. Commun. Soft Comput. Sci. Eng"},{"issue":"6","key":"IJAPUC.2019010104-18","article-title":"Detection of tumor in MRI images using image segmentation.","volume":"2","author":"H. J.Shah","year":"2014","journal-title":"International Journal (Toronto, Ont.)"},{"key":"IJAPUC.2019010104-19","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2014.2360515"},{"issue":"1","key":"IJAPUC.2019010104-20","first-page":"95","article-title":"Efficient algorithm for contrast enhancement of natural images.","volume":"11","author":"S.Lal","year":"2014","journal-title":"The International Arab Journal of Information Technology"},{"key":"IJAPUC.2019010104-21","unstructured":"Kumbhar, U., Patil, V., & Rudrakshi, S. (2013). Enhancement Of Medical Images Using Image Processing In Matlab. International Journal of Engineering Research and Technology."},{"key":"IJAPUC.2019010104-22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/ICSSE.2017.8030827","article-title":"Edge detection based on Fuzzy C Means in medical image processing system.","author":"N. M.Hien","year":"2017","journal-title":"2017 International Conference on System Science and Engineering (ICSSE)"},{"key":"IJAPUC.2019010104-23","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.06.079"},{"key":"IJAPUC.2019010104-24","first-page":"186","article-title":"Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm.","author":"J.Selvakumar","year":"2012","journal-title":"2012 International Conference on Advances in Engineering, Science and Management (ICAESM)"},{"key":"IJAPUC.2019010104-25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.05.004"},{"key":"IJAPUC.2019010104-26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcde.2016.02.002"},{"key":"IJAPUC.2019010104-27","unstructured":"Osiri, X. MD (n.d.). DICOM Samples Image Sets. Retrieved from http:\/\/www.osirix-viewer.com\/"},{"key":"IJAPUC.2019010104-28","unstructured":"BrainWeb. (n.d.). Simulated Brain Database. Retrieved from http:\/\/brainweb.bic.mni.mcgill.ca\/cgi\/brainweb1"},{"key":"IJAPUC.2019010104-29","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1979.11325"}],"container-title":["International Journal of Advanced Pervasive and Ubiquitous Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=224939","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T12:00:51Z","timestamp":1651838451000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJAPUC.2019010104"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":30,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.4018\/ijapuc.2019010104","relation":{},"ISSN":["1937-965X","1937-9668"],"issn-type":[{"value":"1937-965X","type":"print"},{"value":"1937-9668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1]]}}}