178
Views
7
CrossRef citations to date
0
Altmetric
Original Articles

Advance computer analysis of magnetic resonance imaging (MRI) for early brain tumor detection

&
Pages 555-570 | Received 14 May 2019, Accepted 15 Mar 2020, Published online: 15 Apr 2020

References

  • Bahadure NB, Ray AK, Thethi HP. Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging (Hindawi). 2017;2017:1–12.
  • Evelin Sujji G, Lakshmi YVS, Wiselin Ji G. MRI brain image segmentation based on thresholding. Int J Adv Comput Res. 2013;3(8):97–101.
  • Dhanachandra N .Manglem K.Jina Chanu Y. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Eleventh International Multi-Conference on Information Processing. 2015;54:764–771.
  • Goshal D, Pratim Acharjya P. MRI image segmentation using watershed transform. Int J Emerg Technol Adv Eng. 2012;2(4):373–376.
  • Ravichandran D, Nimmatoori R, Ashwin Dhivakar MR. 2016. Performance of wavelet based image compression on medical images for cloud computing.International Journal of Advanced Computer Engineering and Communication Technology. 2016;5(2):5–12.
  • Miranda PAV, Falcao AX, Udupa JK. Cloud bank: a multiple clouds model and its use in MRI brain image segmentation. IEEE International Symposium on Biomedical Imaging: From Nano to Macro.2009:506 –509.
  • Fenshia Singh J Magudeeswara V. Thresholding based method for segmentation of MRI brain images. International conference on ISMAC (IoT in Social, Mobile, Analytics and Cloud). 2017:280–283.
  • Eapenaa M. Joy Angel Ancelitab S. Geetha G. Segmentation of tumors from ultrasound images with PAORGB. 2nd International Symposium on Big Data and Cloud Computing (ISBCC). 2015;50(2015):663–668.
  • Yli-Harja O, Astola J, Neuvo Y. Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation. IEEE Trans Acoust Speech Signal Process. 1991;39(2):395–410.
  • Barner KE, Arce GR. 2004. Nonlinear signal and image processing: theory methods and applications. Boca Raton (FL): CRC Press.
  • Akkoul S, Lédée R, Leconge R, et al. A new adaptive switching median filter. IEEE Signal Process Lett. 2010;17(6):587–590.
  • Kirchner M, Fridrich J. On detection of median filtering in digital images. Proc SPIE Electron Imag Media Forensics Secu II. 2010;7541(1-12):754110.
  • Zhang Y, Li S, Wang S, et al. Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Process Lett. 2014;21(3):275–280.
  • Tang H, Ni R, Zhao Y, et al. Median filtering detection of small-size image based on CNN. J Vis Commun Image Represent. 2018;51:162–168.
  • Zhang Z, Han Dezert DJ, et al. A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning. Signal Process. 2018;147:173–189.
  • Ahmad M, Jung LT, Bhuiyan A-A. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing. Comput Methods Programs Biomed. 2017;149:11–17.
  • Iuona A, Direko-Lub C, Uahc M. Review of MRI-based brain tumor image segmentation using deep learning methods. 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS. 2016;102(2016):317–324.
  • Kagadis GC, Kloukinas C, Moore K, et al. Cloud computing in medical imaging. Am Assoc Phys Med. 2013; 40(7):1–13.
  • Lin Y-C, Yu C-S, Lin Y-J. Enabling large-scale biomedical analysis in the cloud. BioMed Res Int. 2013;2013:1–6.
  • Perez A, Gonzalez RC. An iterative thresholding algorithm for image segmentation. IEEE Trans Pattern Anal Mach Intell. 1987;9(6):742–751.
  • Topgaard D. Multidimensional diffusion MRI. J Magn Reson. 2017;275:98–113.
  • Mirarab A, Ghasemi Fard N, Shamsi M. A cloud solution for medical image processing. Int J Eng Res Appl. 2014;4(7):74–82.
  • Yan Y,.Huang L.Large-scale image processing research cloud clod computing. The Fifth International Conference on Cloud Computing, Grids, and Virtualization. 2014;88–93.
  • Thai DH, Mentch L. Multiphase segmentation for simultaneously homogeneous and textural image. Appl Math Comput. 2018;335:146–181.
  • Hamasuna Y, Endo Y, Miyamoto S. On Mahalanobis distance based fuzzy c-means clustering for uncertain data using penalty vector regularization. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, 2011;810–815.
  • Hore P, Hall LO, Goldgof DB, et al. A scalable framework for segmenting magnetic resonance images. J Sign Process Syst Sign Image Video Technol. 2009;54(1-3):183–203.
  • Ahmed 2MN, Yamany SM, Mohamed N, et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging. 2002;21(3):193–199.
  • Dembele D, Kastner P. Fuzzy c-means method for clustering microarray data. Bioinformatics. 2003;19(8):973–980.
  • Biswal B, Dash PK, Panigrahi BK. Power quality disturbance classification using fuzzy c-means algorithm and adaptive particle swarm optimization. IEEE Trans Ind Electron. 2009;56(1):212–220.
  • Pal NR, Bezdek JC. On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst. 1995;3(3):370–379.
  • Jain AK. Data clustering: 50 years beyond k-means. Pattern Recognit. Lett. 2010;31(8):651–666.
  • Carson C, Belongie S, Greenspan H, et al. Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans Pattern Anal Machine Intell. 2002;24(8):1026–1038.
  • Shi R, Ngan KN, Li S, et al. Interactive object segmentation in two phases. Signal Process Image Commun. 2018;65:107–114.
  • Wang L, Chen G, Shi D, et al. Active contours driven by edge entropy fitting energy for image segmentation. Signal Process. 2018;149:27–35.
  • Hardie AD, Egbert RE, Rissing MS. Improved differentiation between hepatic hemangioma and metastases on diffusion-weighted MRI by measurement of standard deviation of apparent diffusion coefficient. Clin Imaging. 2015;39(4):654–658.
  • Budde MD, Skinner NP. Diffusion MRI in acute nervous system injury. J Magn Reson. 2018;292:137–148.
  • Holambe SN, Shinde UB, Kshirsagar P. A brief review on blind image quality evaluation methods. IJCA. 2017;163(6):24–28.
  • Mittal A, Soundararajan R, Bovik AC. Making a “completely blind” image quality analyzer. IEEE Signal Process Lett. 2013;20(3):209–212.
  • Zhang. L. Zhang. L. and Bovik AC. A Feature Enriched Completely Blind Image Quality Evalutor.IEEE Transactions on Image Processing. 2015;24(8):2579–2591.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.