Ensemble selection for feature-based classification of diabetic maculopathy images

Computers in Biology and Medicine

Volumn 43, Issue 12, 2013, Pages 2156-2162

  Chowriappa, Pradeep ^a   Dua, Sumeet ^a   Rajendra Acharya, U ^b,c   Muthu Rama Krishnan, M ^b  

^a Louisiana Tech University   (United States)

^b DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^c UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Decision system; Diabetic retinopathy; Ensemble classifier; Feature extraction; Fundus imaging; Image texture

Indexed keywords

CLASSIFICATION ACCURACY; CLINICALLY SIGNIFICANT MACULAR EDEMA; DECISION SYSTEMS; DIABETIC RETINOPATHY; ENSEMBLE CLASSIFIERS; FEATURE-BASED CLASSIFICATION; FUNDUS IMAGING; SEQUENTIAL MINIMAL OPTIMIZATION;

EYE PROTECTION; FEATURE EXTRACTION; IMAGE TEXTURE;

OPTIMIZATION;

ACCURACY; ARTICLE; BAYES THEOREM; CLINICAL FEATURE; CONCEPTUAL FRAMEWORK; DIABETIC MACULOPATHY; DIABETIC RETINOPATHY; DISEASE CLASSIFICATION; DISEASE SEVERITY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MEDICAL DECISION MAKING; OPHTHALMOSCOPY; PRIORITY JOURNAL; RETINA MACULA EDEMA; RETINA MACULOPATHY; SCREENING TEST; SYMPTOM ASSESSMENT; VALIDITY;

DECISION SYSTEM; DIABETIC RETINOPATHY; ENSEMBLE CLASSIFIER; FEATURE EXTRACTION; FUNDUS IMAGING; IMAGE TEXTURE;

DIABETIC RETINOPATHY; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; FUNDUS OCULI; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACULAR EDEMA; MALE;

EID: 84887004005     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.10.003     Document Type: Article

References (20)

1. C. Sinthanayothin, V. Kongbunkiat, S. Phoojaruenchanachai, A. Singalabanija, Automated screening system for diabetic retinopathy, in: Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, Aizu, Japan, 2003, pp. 915-920.
2. Yen G.G., Leong W.-F. A sorting system for hierarchical grading of diabetic fundus images: a preliminary study. IEEE Trans. Inf. Tech. Biomed. 2008, 12:118-130.
3. Kahai P., Namuduri K.R., Thompson H. A decision support framework for automated screening of diabetic retinopathy. Int. J. Biomed Imaging 2006, 2006:1-8.
4. Nayak J., Bhat P.S., Acharya U.R. Automatic identification of diabetic maculopathy stages using fundus images. J. Med. Eng. Tech. 2009, 33:119-129. 10.1080/03091900701349602.
5. Ang M.H., Acharya U.R., Sree S.V., Lim T.-C., Suri J.S. Computer-based identification of diabetic maculopathy stages using fundus images. Multi modality state of the art medical image segmentation and registration methodologies. Science+Business Media, LLC 2011, vol. 1:319. Springer, New York.
6. Acharya R., Dua S., Du X., Chua C., Sree V. Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans. Inf. Tech. Biol. 2011, 15:1.
7. Dua S., Acharya R.U., Chowriappa P., Sree S.V. Wavelet-based energy features for glaucomatous image classification. IEEE Trans. Info. Tech. Biomed. 2012, 16:80-87. http://dx.doi.org/19.1109/TITB.2011.2176540.
8. R. Caruana, A. Munson, N.-M. Ru, Getting the most out of ensemble selection, in: Proceedings of the 6th International Conference on Data Mining, ICDM'06, pp. 828-833.
9. Mandelbrot B.B. The Fractal Geometry of Nature 1982, W H Freeman, New York.
10. Biswas M.K., Ghose T., Guha S., Biswas P.K. Fractal dimension estimation for texture images: a parallel approach. Pattern Recognition Lett. 1998, 19.
11. K.I. Laws, Rapid texture identification, in: Proceedings of SPIE Image Processing for Missile Guidance, vol. 238, 1980, pp. 376-380.
12. Petrou M., Sevilla P.G. Image Processing - Dealing with Texture. s.l 2006, John Wiley and Sons, West Sussex, England.
13. Ojala T., Pietikäinen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. 2002, 24:971-987.
14. Gonzalez R.C., Woods R.E. Digital Image Processing 2002, 655-659. Prentice Hall, New York. 2nd ed.
15. B.S. Manjunath, W.Y. Ma, Texture Features for browsing and retrieval of image data, IEEE Trans. Pattern Anal., vol. 18, pp. 837-842.
16. Liao S., Law M.W.K., Chung A.C.S. Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 2009, 18:1107-1118.
17. H. Zhang, L. Jiang, and J. Su, Hidden naive Bayes, in: Proceedings of the 20th National Conference on Artificial Intelligence, 2005, pp. 919-924.
18. Keerthi S.S., Shevade S.K., Bhattacharyya C., Murthy K.R.K. Improvement to Platt's SMO algorithm for SVM classifier design. Neural Comput. 2001, 13:637-649.
19. Quinlan R. C4.5: Programs for Machine Learning 1993, Morgan Kaufmann Publishers, San Mateo, CA.
20. P.-Y. Xia, X.-Q. Ding, B.-N. Jiang, A GA-based feature selection and ensemble learning for high-dimensional datasets, in: Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, 2009, pp. 7-12.