LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Unknown
Subjects: RC0254, QA76

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Qureshi, H., Sertel, O., Rajpoot, N., Wilson, R., Gurcan, M.: Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classi¯cation. In: Proceedings 11th Medical Image Computing and ComputerAssisted Intervention (MICCAI'2008). (2008)
    • 2. Lessmann, B., Hans, V., Degenhard, A., Nattkemper, T.W.: Feature space exploration of pathology images using content-based database visualization. In: Proceedings SPIE Medical Imaging. (2006)
    • 3. Qureshi, H., Rajpoot, N., Masood, K., Hans, V.: Classi¯cation of meningiomas using discriminant wavelet packets and learning vector quantization. In: Proceedings of Medical Image Understanding and Analysis. (2006)
    • 4. Wirjadi, O., Breuel, T., Feiden, W., Kim, Y.J.: Automated feature selection for the classi¯cation of meningioma cell nuclei. In Handels, H., Ehrhardt, J., Horsch, A., Meinzer, H.P., Tolxdor®, T., eds.: Bildverarbeitung fr die Medizin. Informatik Aktuell, Springer (2006) 76{80
    • 5. Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2008). (May 2008) 284{287
    • 6. Sahoo, P., Soltani, S., Wong, A.: Survey: a survey of thresholding techniques. Computer Vision Graphics Image Processing 41 (1988) 233{260
    • 7. Francis, I., Adeyanju, M., George, S., Junaid, T., Luthra, U.: Manual versus image analysis estimation of pcna in breast carcinoma. Anal. Quant. Cytol. Histol. 22 (2000) 11{16
    • 8. Petushi, S., Garcia, F.U., Haber, M.M., Katsinis, C., Tozeren, A.: Large-scale computations on histology images reveal grade-di®erentiating parameters for breast cancer. BMC Medical Imaging 6 (2006) 14
    • 9. Ji, Q., Engel, J., Craine, E.: Texture analysis for classi¯cation of cervix lesions. IEEE Transactions on Medical Imaging 19(11) (Nov. 2000) 1144{1149
    • 10. Law, A.K.W., Lam, K.Y., Lam, F.K., Wong, T.K.W., Poon, J.L.S., Chan, F.H.Y.: Image analysis system for assessment of immunohistochemically stained proliferative marker (mib-1) in oesophageal squamous cell carcinoma. Computer Methods and Programs in Biomedicine 70(1) (2003) 37 { 45
    • 11. Schupp, S., Elmoataz, A., Fadili, J., Herlin, P., Bloyet, D.: Image segmentation via multiple active contour models and fuzzy clustering with biomedical applications. In: Proceedings. 15th International Conference on Pattern Recognition. Volume 1. (2000) 622{625 vol.1
    • 12. M. Gurcan et al.: Histopathological image analysis: A review. Submitted to Medical Image Analysis (2009)
    • 13. Demir, C., Gultekin, S., Yener, B.: Learning the topological properties of brain tumors. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2(3) (July-Sept. 2005) 262{270
    • 14. Hamilton, P., Bartels, P., Thompson, D., Anderson, N., Montironi, R., Sloan, J.: Automated location of dysplastic ¯elds in colorectal histology using image texture analysis. J. Pathol. 182 (1997) 68{75
    • 15. Esgiar, A., Naguib, R., Sharif, B., Bennett, M., Murray, A.: Microscopic image analysis for quantitative measurement and feature identi¯cation of normal and cancerous colonic mucosa. IEEE Trans. Inf. Technol. Biomed. 2 (1998) 197{203
    • 16. Gibson, D., Gaydecki, P.: De¯nition and application of a Fourier domain texture measure: applications to histological image analysis. Comput. Biol. Med. 25 (1995) 551{557
    • 17. Qureshi, H., Wilson, R., Rajpoot, N.: Optimal wavelet basis for wavelet packets based meningioma subtype classi¯cation. In: Proceedings 12th Medical Image Understanding and Analysis (MIUA'2008). (2008)
    • 18. Jafari-Khouzani, K., Soltanian-Zadeh, H.: Multiwavelet grading of pathological images of prostate. IEEE Transactions on Biomedical Engineering 50(6) (2003) 697{704
    • 19. Cross, S.: Fractals in pathology. J. Pathol. 182 (1997) 1{8
    • 20. Cross, S., Bury, J., Silcocks, P., Stephenson, T., Cotton, D.: Fractal geometric analysis of colorectal polyps. J. Pathol. 172 (1994) 248{262
    • 21. Cross, S., Howat, A., Stephenson, T., Cotton, D., Underwood, J.: Fractal geometric analysis of material from molar and non-molar pregnacies. J. Pathol. 173 (1994) 115{118
    • 22. Julesz, B., Bergen, J.R.: Textons, the fundamental elements in preattentive vision and perception of textures. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1987)
    • 23. Coifman, R.R., Wickerhauser, M.V.: Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory 38(2) (1992) 713{718
    • 24. Jain, A., Zongker, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19(2) (1997) 153{158
    • 25. Bhalerao, A., Rajpoot, N.: Discriminant feature selection for texture classi¯cation. In: Proceedings British Machine Vision Conference (BMVC'2003). (2003)
    • 26. Haralick, R., Shanmugan, K., Dinstein, J.: Textural features for image classi¯cation. IEEE Trans. Syst. Man Cybern. 3 (1973) 610{621
    • 27. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. (2001) Software available at urlhttp://www.csie.ntu.edu.tw/~cjlin/libsvm.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article