Degradation adaptive texture classification: A case study in celiac disease diagnosis brings new insight

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8815, Issue , 2014, Pages 263-273

  Gadermayr, Michael ^a   Uhl, Andreas ^a   Vécsei, Andreas ^b  

^a UNIVERSITY OF SALZBURG   (Austria)

^b MEDICAL UNIVERSITY OF VIENNA   (Austria)

Author keywords

Adaptive classification; Celiac disease; Endoscopic images

Indexed keywords

ENDOSCOPY; IMAGE ANALYSIS; TEXTURES; DIAGNOSIS;

ADAPTIVE CLASSIFICATION; ADAPTIVE TEXTURES; CELIAC DISEASE; CLASSIFICATION ACCURACY; COMPUTER AIDED; DEGREE OF SIMILARITY; ENDOSCOPIC IMAGE; IMAGE DATABASE;

COMPUTER AIDED DIAGNOSIS;

EID: 84921521309     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-11755-3_30     Document Type: Conference Paper

References (23)

1. Gadermayr, M., Uhl, A.: Degradation adaptive texture classification. In: IEEE International Conference on Image Processing 2014 (ICIP 2014) (October 2014) (accepted)
2. Kylberg, G.: The kylberg texture dataset v. 1.0. Technical Report 35, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Sweden (2011)
3. Ciaccio, E.J., Tennyson, C.A., Lewis, S.K., Krishnareddy, S., Bhagat, G., Green, P.: Distinguishing patients with celiac disease by quantitative analysis of videocapsule endoscopy images. Comp. Methods and Prog. in Biomedicine 100(1), 39-48 (2010)
4. Ciaccio, E.J., Tennyson, C.A., Bhagat, G., Lewis, S.K., Green, P.H.R.: Classification of videocapsule endoscopy image patterns: comparative analysis between patients with celiac disease and normal individuals. BioMedical Engineering Online 9(1), 1-12 (2010)
5. Vécsei, A., Amann, G., Hegenbart, S., Liedlgruber, M., Uhl, A.: Automated marshlike classification of celiac disease in children using an optimized local texture operator. Computers in Biology and Medicine 41(6), 313-325 (2011)
6. Hegenbart, S., Uhl, A., Vécsei, A., Wimmer, G.: Scale invariant texture descriptors for classifying celiac disease. Medical Image Analysis 17(4), 458-474 (2013)
7. Gadermayr, M., Liedlgruber, M., Uhl, A., Vécsei, A.: Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease. Computer Methods and Programs in Biomedicine 112(3), 694-712 (2013)
8. Hegenbart, S., Uhl, A., Vécsei, A.: Impact of endoscopic image degradations on lbp based features using one-class svm for classification of celiac disease. In: Proceedings of the 7th International Symposium on Image and Signal Processing and Analysis (ISPA 2011), pp. 715-720 (2011)
9. Hegenbart, S., Uhl, A., Vécsei, A.: On the implicit handling of varying distances and gastrointestinal regions in endoscopic video sequences with indication for celiac disease. In: Proceedings of the IEEE International Symposium on Computer-Based Medical Systems (CBMS 2012), pp. 1-6 (2012)
10. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213-226. Springer, Heidelberg (2010)
11. Giacinto, G., Roli, F.: Methods for dynamic classifier selection. In: Proceedings of the Intern. Conf. on Image Analysis and Processing (ICIAP 1999), pp. 659-664 (1999)
12. Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T., Sa, G.: A no-reference perceptual blur metric. In: IEEE International Conference on Image Processing (ICIP 2002), pp. 57-60 (2002)
13. Gadermayr, M., Hegenbart, S., Uhl, A.: Scale-adaptive texture classification. In: Proceedings of 22nd International Conference on Pattern Recognition (ICPR 2014) (August 2014) (accepted)
14. Oberhuber, G., Granditsch, G., Vogelsang, H.: The histopathology of coeliac disease: time for a standardized report scheme for pathologists. European Journal of Gastroenterology and Hepatology 11, 1185-1194 (1999)
15. Haralick, R.M., Dinstein, I., Shanmugam, K.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3, 610-621 (1973)
16. Rautkorpi, R., Iivarinen, J.: A novel shape feature for image classification and retrieval. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 753-760. Springer, Heidelberg (2004)
17. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51-59 (1996)
18. Liao, S.C., Zhu, X.X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828-837. Springer, Heidelberg (2007)
19. Gadermayr, M., Uhl, A., Vécsei, A.: Barrel-type distortion compensated fourier feature extraction. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Li, B., Porikli, F., Zordan, V., Klosowski, J., Coquillart, S., Luo, X., Chen, M., Gotz, D. (eds.) ISVC 2013, Part I. LNCS, vol. 8033, pp. 50-59. Springer, Heidelberg (2013)
20. Gadermayr, M., Liedlgruber, M., Uhl, A., Vécsei, A.: Shape curvature histogram: A shape feature for celiac disease diagnosis. In: Menze, B., Langs, G., Montillo, A., Kelm, M., M¨uller, H., Tu, Z. (eds.) MCV 2013. LNCS, vol. 8331, pp. 175-184. Springer, Heidelberg (2014)
21. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 886-893 (2005)
22. Duin, R., Juszczak, P., Paclík, P., Pekalska, E., de Ridder, D., Tax, D., Verzakov, S.: PR-Tools4.1, A matlab toolbox for pattern recognition (2007)
23. McNemar, Q.: Note on the sampling error of the difference between correlated proportions of percentages. Psychometrika 12(2), 153-157 (1947)