171
Views
9
CrossRef citations to date
0
Altmetric
Innovation

Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices

&
Pages 1-9 | Received 16 Feb 2012, Accepted 10 Jul 2012, Published online: 24 Oct 2012

References

  • Wei, K., He, B.Z.T., Shen, X., 2007, A novel method for segmentation of CT head images. In Proceedings of the 1st International Conference on Bio Informatics and Biomedical Engineering (ICBBE’07), Huazhong Normal University, Wuhan, p 717–720.
  • Lauric, A., Frisken, S., 2009, Soft segmentation of CT brain data. Technical Report TR-2007-3, Tufts University, Halligan Hall Room 102, 161 College Ave, Medford MA 02155, USA.
  • Sharma, N., Ray, A.K., Sharma, S., Shukla, K.K., Pradhan, S., Aggarwal, L.M., 2008, Segmentation and classification of medical images using texture primitive features: Application of BAM-type artificial neural network. Journal of Medical Physics, 33, 120–126.
  • Tong, H.L., Faizal, M., Fauzi, A., Komiya, R., 2009, Segmentation of CT Brain Images using unsupervised clustering’s. Journal of Visualization, 12, 31–138.
  • Rajendran, P., Madheswaran, M., 2009, An improved image mining technique for brain tumor classification using efficient classifier. International Journal of Computer Science and Network Security, 6, 107–116.
  • Ganesan, R., Radhakrishnan, R., 2009, Segmentation of computed tomography brain images using genetic algorithm. International Journal of Soft Computing, 4, 157–161.
  • Kharrat, A., Gasmi, K., Ben Messaoud, M., Benamrane, N., Abid, M., 2010, An hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo Journal of Sciences, 17, 71–82.
  • Padma, A., Sukanesh, R., 2011, Texture feature based analysis of segmenting soft tissues from brain CT images using BAM-type artificial neural network. Journal of Information Engineering and Applications, 1, 34–42.
  • Pisano, E.D., Cole, E.B., Hemminger, B.M., 2000, Image processing algorithms for digital mammography: a pictorial essay. Journal of Radiographics, 20, 1479–1491.
  • Hall, L.O., Bensaid, A.M., Clarke, L., Velthuizan, R.P., Silbiger, M., Bezdek, J.C., 1992, A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transaction on Neural Networks, 3, 672–681.
  • Xu, D.H., Kurani, A., Furst, J.D., Raicu, D.S., 2004, Run-length encoding for volumetric texture. The 4th IASTED International Conference on Visualization, Imaging, and Image Processing - VIIP 2004, Marbella, Spain, September 6–8, 2004.
  • Galloway, M.M., 1975, Texture analysis using gray level run lengths. Computer Graphics & Image Processing, 4, 172–179.
  • Tang, X., 1997, Dominant run length method for image classification. Woods Hole Report – 97-07, Woods Hole Oceanographic Institution, Woods Hole, Massachusettes 025423..
  • Tang, X., 1996, Transform texture classification. PhD Thesis, MIT/WHOI-97-06,Woods Hole Oceanographic Institution, Woods Hole, Massachusettes 025423.
  • Khuzi, M., Besar, R., Wan Zaki, W.M.D., Ahmad, N.N., 2009, Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomedical Imaging and Intervention Journal, 5, 109–119.
  • Haralick, R.M., Shanmugam, K., Dinstein, I., 1973, Texture features for Image classification. IEEE Transaction on System, Man & Cybernetics, 3, 610–621.
  • Weingessel, A., Hornik, K., 2000, Local PCA algorithms. IEEE Transaction on Neural Networks, 11, 1242–1250.
  • Haralick, R.M., Shapiro, L.G., 1992, Computer and robot vision. Addison-Wesley Publishing Co, The University of Michigan.
  • Tang, X., 1998, Texture information in run length matrices. IEEE Transaction on Image Processing, 7, 234–243.
  • El-Naqa, I., Yang, Y., Wernick, M.N., Galatsanos, N.P., Nishikawa, R.M., 2002, A support vector machine approach for detection of micro calcifications. IEEE Transaction on Medical Imaging, 21, 1552–1563.
  • Liao, R.Y.-Y., Tsui, P.-H., Yeh, C.-K., 2009, Classification of benign and malignant breast tumors by ultrasound B-scan and Nakagami-based images. Journal of Medical and Biological Engineering, 30, 307–312.
  • Kim, J.K., Park, H.W., 1999, Statistical textural features for detection of micro calcifications in digitized mammograms. IEEE Transaction on Medical Imaging, 18, 231–238.
  • Mellors, R.C., NEOPLASIA. Biological characteristics of benign and malignant neoplasms. Available at: www.medpath.info/MainContent/Neoplasia/Neoplasia_02.html.
  • Ben Ayed, I., Mitiche, A., Belhadj, Z., 2005, Multi-level set partitioning on synthetic aperture radar images. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27, 793–800.

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.