Automated diagnosis of oral cancer using higher order spectra features and local binary pattern: A comparative study

Technology in Cancer Research and Treatment

Volumn 10, Issue 5, 2011, Pages 443-455

  Krishnan, M M R ^a   Acharya, U R ^b   Chakraborty, C ^a   Ray, A K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

Author keywords

Classifier; Higher order spectra; Histopathology; Local binary pattern; Oral submucous fibrosis; Support vector machine

Indexed keywords

ARTICLE; CANCER DIAGNOSIS; CANCER SCREENING; COMPARATIVE STUDY; DIAGNOSTIC ACCURACY; EVALUATION; HIGHER ORDER SPECTRA; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; LOCAL BINARY PATTERN; MEDICAL PARAMETERS; MOUTH CANCER; MOUTH DISEASE; MOUTH MUCOSA; ORAL SUB MUCOUS FIBROSIS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 80052918395     PISSN: 15330346     EISSN: None     Source Type: Journal    
DOI: 10.7785/tcrt.2012.500221     Document Type: Article

References (60)

1. Daftary, D. K., Murti, P. R., Bhonsale, R. B., Gupta, P. C., Mehta, F. S., Pindborg, J. J. Oral precancerous lesions and conditions of tropical interest. In: Prabhu, S. R., Wilson, D. F., Daftary, D. K., Johnson, N. W., Editors. Oral diseases in the tropics. Oxford: Oxford University Press, 402-424 (1993).
2. Banoczy, J., Gintner, Z., Dombi, C. Toboco use and Oral leucoplakia. Journal of Dental Education 65(4), 322-327 (2001).
3. Burkhardt, A. Advanced methods in the evaluation of premalignant lesions and carcinoma of the oral mucosa. Journal of Oral Pathology 14, 751-758 (1985).
4. Paul, R. R., Mukherjee, A., Dutta, P. K., Banerjee, S., Pal, M., Chatterjee, J., Chaudhuri, K., Mukkerjee, K. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition. Journal of Clinical Pathology 58, 932-938 (2005).
5. Muthu Rama Krishnan, M., Shah, P., Pal, M., Chakraborty, C., Paul, R. R., Chatterjee, J., Ray, A. K. Structural markers for normal oral mucosa and oral sub-mucous fibrosis, Micron 41(4), 312-320 (2010a).
6. Prabhu, S. R., Wilson, D. F., Daftary, D. K., Jhonson, N. W. Oral diseases in the Tropics: Oxford University Press (1992).
7. Tilakaratne, W., Klinikowski, M., Saku, T., Peters, T., Warnakula suriya, S. Oral submucous fibrosis: Review on aetiology and pathogenesis. Oral Oncology 42, 561-568 (1997).
8. Al-Kadi, O. S. Texture measures combination for improved menin gioma classification of histological images, Pattern Recognition 43, 2043-2053 (2010).
9. Shuttleworth, J., Todman, A., Norrish, M., Bennett, M. Learning his topathological microscopy. Pattern Recognition and Image Analysis, Pt 2, Proceedings 3687, 764-772 (2005).
10. Gilles, F. H., Tavare, C. J., Becker, L. E., Burger, P. C., Yates, A. J., Pollack, I. F., Finlay, J. L. Pathologist interobserver variability of histologic features in childhood brain tumors: Results from the CCG-945 study. Pediatric and Developmental Pathology 11, 108-117 (2008).
11. Grootscholten, C., Bajema, I. M., Florquin, S., Steenbergen, E. J., Peutz-Kootstra, C. J., Goldschmeding, R., Bijl, M., Hagen, E. C., Van, H. C., Houwelingen, D. R., Berden, J. H. M. Interobserver agreement of scoring of histopathological characteristics and classification of lupus nephritis, Nephrology Dialysis Transplantation 23, 223-230 (2008).
12. Duncan, J. S., Ayache, N. Medical image analysis: Progress over two decades and the challenges ahead. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 85-106 (2000).
13. Lessmann, B., Nattkemper, T. W., Hans, V. H., Degenhard, A. A method for linking computed image features to histological semantics in neuropathology. Journal of Biomedical Informatics 40, 631-641 (2007).
14. Qian, W., Zhukov, T., Song, D. S., Tockman, M. S. Computerized analysis of cellular features and biomarkers for cytologic diagnosis of early lung cancer. Analytical and Quantitative Cytology and Histology 29, 103-111 (2007).
15. Sertel, O., Kong, J., Shimada, H., Catalyurek, U., Saltz, J. H., Gurcan, M. Computer-aided prognosis of neuroblastoma: Classification of stromal development on whole-slide images, Medical Imaging: Computer- Aided Diagnosis 6915, 9150-9151(2008).
16. Ferrari, R. J., Rangayyan, R. M., Desautels, J. E. L., Frere, A. F., Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets, IEEE Transactions on Medical Imaging 20(9), 953-964 (2001).
17. Wu, C. M., Chen, Y. C., Hsieh, K. S. Texture features for classifica tion of ultrasonic liver images. IEEE Transactions on Medical Imaging 11(2), 141-152 (1992).
18. Marghani, K. A., Dlay, S. S., Sharif, B. S., Sims, A. Morphological and texture features for cancers tissues microscopic images. Medical Imaging and Image Processing 5032, 1757-1764 (2003).
19. Alexandratou, E., Yova, D., Gorpas, D., Maragos, P., Agrogiannis, G., Kavantzas, N. Texture analysis of tissues in Gleason grading of prostate cancer. Imaging Manipulation and Analysis of Biomolecules Cells and Tissues, 85904-85904 (2008).
20. Wiltgen, M., Gerger, A., Wagner, C., Bergthaler, P., Smolle, J. Evaluation of texture features in spatial and frequency domain for automatic discrimination of histologic tissue. Analytical and Quantitative Cytology and Histology 29, 251-263 (2007).
21. Wittke, C., Mayer, J., Schweiggert, F. On the classification of prostate carcinoma with methods from spatial statistics. IEEE Transactions on Information Technology in Biomedicine 11, 406-414 (2007).
22. Thiran, J. P., Macq, B. Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Transactions on Biomedical Engineering 43, 1011-1020 (1996).
23. Seker, H., Odetayo, M. O., Petrovic, D., Naguib, R. N. G. A Fuzzylogic based-method for prognostic decision making in breast and prostate cancers. IEEE Transactions on Information Technology in Biomedicine 7, 114-122 (2003).
24. Estevez, J., Alayon, S., Moreno, L., Sigut, J., Aguilar, R. Cytologicalimage analysis with a genetic fuzzy finite state machine, Computer Methods and Programs in Biomedicine 80, S3-S15 (2005).
25. Muthu Rama Krishnan, M., Shah, P., Chakraborty, C., Ray, A. K. Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images, Journal of Medical Systems, DOI: 10.1007/s10916-010-9550-8 (Article In Press) (2010b).
26. Unay, D., Ekin, A. Intensity versus texture for medical image search and retrieval. In: Proceedings of the 5th IEEE international symposium on biomedical imaging: from nano to macro (ISBI 2008), 241-244 (2008).
27. Oliver, A., Llado ́, X., Freixenet, J., Marti ́, J. False positive reduction in mammographic mass detection using local binary patterns. In: Proceedings of the medical image computing and computer-assisted intervention (MICCAI 2007), 286-293 (2007).
28. Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M. Cardiac State Diagnosis Using Higher Order Spectra of Heart Rate Variability. J Med Eng Technol 32, 2145-155 (2006).
29. Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M. Analysis of epileptic EEG signals using higher order spectra. Conf Proc IEEE Eng Med Biol Soc 1, 6495-6498 (2007).
30. Acharya, U. R., Ng, E. Y. K., Suri, J. S. Image modeling of human eye. Artech House, Norwood (2008a).
31. Acharya, U. R., Chua, K. C., Ng, E. Y. K., Wei, W., Chee, C. Application of higher order spectra for the identification of diabetes retinopathy stages. Journal of Medical Systems 32(6), 481-488 (2008b).
32. Chandran, V., Carswell, B., Boashash, B., Elgar, S. L. Pattern Recognition Using Invariants Defined from Higher Order Spectra: 2-D Image Inputs. IEEE Transcations on image processing 6, 703-712 (1997).
33. Shao, Y., Celenk, M. Higher-order spectra (HOS) invariants for shape recognition. Pattern Recognition 34, 2097-2113 (2001).
34. Tan, T. G., Acharya, U. R., Ng, E. Y. K. Automated identification of eye diseases using higher order spectra. J Mech Med Biol (In Press) (2008).
35. Nieman, L. T., Jakovljevic, M., Sokolov, K. Compact beveled fiber optic probe design for enhanced depth discrimination in epithelial tissues. Optics Express 17(4), 2780-2796 (2009).
36. Sokolov, K., Tam, J., Tam, J., Travis, K., Larson, T., Aaron, J., Harrison, N., Emelianov, S., Johnston, K. Cancer imaging and therapy with metal nanoparticles. 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC (2009).
37. Roblyer, D., Kurachi, C., Stepanek, V., Schwarz, R. A., Williams, M. D., El-Naggar, A. K. J., Lee, J., Gillenwater, A. M., Kortum, R. R. Comparison of multispectral wide-field optical imaging modalities to maximize image contrast for objective discrimination of oral neoplasia, Journal of Biomedical Optics 15(6), (2010).
38. Roblyer, D., Kortum, R. R. Optical Diagnostics for Early Detection of Oral Cancer, ADHA Access, 22-25 (2010).
39. Jain, A. K., Farrokhnia, F. Unsupervised texture segmentation using. Gabor filters, Pattern Recognition 24, 1167-1186 (1991).
40. Sijbers, J., Scheunders, P., Verhoye, M., Linden, A. V. D., Dyck, D. V. Raman, E. Watershed segmentation of 3D MR data for volume quantization. Magnetic Resonance Imaging 15, 679-688 (1997).
41. Ojala, T., Pietikainen, M., Maenpaa, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971-987 (2002).
42. Leavers, V. F. Use of the two-dimensional Radon transforms to generate a taxonomy of shape for the characterization of abrasive powder particles. Pattern Analysis and Machine Intelligence 22, 1411-1423 (2000).
43. Chua, K. C., Chandranb, V., Acharya, R. U., Lim, C. M. Application of higher order statistics/spectra in biomedical signals-A review, Journal of Medical Engineering and Physics 32, 679-689 (2010).
44. Nikias, C. L., Petropulu, A. P. Higher-order spectra analysis: a nonlinear signal processing framework. Prentice Hall, Englewood Cliffs (1993).
45. Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M. Analysis of 45 epileptic EEG signals using higher order spectra, Journal of Medical Engineering and Technology 33, 42-50 (2009).
46. Inouye, T., Shinosaki, K., Sakamoto, H., Toi, S., Ukai, S., Iyama, A., Katsuda, Y., Hirano, M. Quantification of EEG irregularity by use of the entropy of the power spectrum, Electroencephalography & Clinical Neurophysiology 79, 204-210 (1991).
47. Ng, T. T., Chang, S. F., Sun, Q. Blind Detection of Photomontage. Using Higher Order Statistics, IEEE International Symposium on Circuits and Systems (ISCAS), Vancouver, Canada (2004).
48. Huang, C. L., Liao, H. C., Chen, M. C. Prediction model building and feature selection with support vector machines in breast cancer diagnosis, Expert Systems with Applications 34, 578-587 (2008).
49. Burges, J., Christopher, C. A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery 2, 121-167 (1998).
50. Vapnik, V. Statistical learning theory, 2nd Edn, Wiley, New York (1998).
51. El-Naqa, I., Yang, Y., Wernick, M. N., Galatians, N. P., Nishikawa, M. R. A Support Vector Machine approach for detection of microcalcifications, IEEE Transactions on medical imaging 21, 1552-1563 (2002).
52. Steve Gunn, R. Support Vector Machines for Classification and Regression, Technical report. 1-66 (1998).
53. Hsu, C. W., Lin, C. J. A comparison of methods for multi class support vector machines. IEEE Trans. on Neural Networks 13, 415-425 (2002).
54. Weston, J., Watkins, C. Multi-class support vector machines Technical Report, Department of Computer Science, Royal Holloway, University of London, Egham (1998).
55. Lei, H., Govindaraju, V. Half-Against-Half Multi-class Support Vector Machines. Proceeding Sixth International Work shop on Multiple Classifier Systems. Springer, Berlin, 156-492 (2005).
56. Platt, C. J., Chrisianini, N., Shawe-Taylor, J. Large Margin DAGs for multiclass classification. Adv Neural Inf Process Syst 12, 547-553 (2000).
57. Gun, A. M., Gupta, M. K., Dasgupta, B. Fundamentals of statistics (Vol I & II), world press private Ltd, 4th edition (2008).k-fold cross validation information available at http://www.cs.cmu.edu/~schneide/tut5/node42.html (last accessed August 2010).
58. Advantages of Local Binary Pattern information available at. http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html (last accessed August 2010).
59. Landini, G., Othman, I. E. Quantification of Local Architecture. Changes Associated with Neoplastic Progression in Oral Epithelium using Graph Theory, Fractals in Biology and Medicine IV, Part 3, pp. 193-201 (2005).
60. Krishnan, M. M. R., Choudhary, A., Chakraborty, C., Ray, A. K., Paul, R. R. Texture based segmentation of epithelial layer from oral histological images, Micron 42(6), pp. 632-641 (2011).