Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria

Pattern Recognition

Volumn 40, Issue 7, 2007, Pages 1899-1910

  Castañón, César A B ^a   Fraga, Jane S ^a   Fernandez, Sandra ^a   Gruber, Arthur ^a   da F Costa, Luciano ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Avian coccidiosis; Eimeria; Feature extraction; Image processing; Pattern classification; Real time systems; Remote diagnosis; Shape analysis

Indexed keywords

BAYESIAN NETWORKS; FEATURE EXTRACTION; GAUSSIAN DISTRIBUTION; INTERFACES (COMPUTER); PROTOZOA; REMOTE SENSING;

AVIAN COCCIDIOSIS; EIMERIA; REMOTE DIAGNOSIS; SHAPE ANALYSIS;

IMAGE RECOGNITION;

EID: 33947724265     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.12.006     Document Type: Article

References (49)

1. Comaniciu D., Meer P., and Foran D. Image-guided decision support system for pathology. Mach. Vision Appl. 11 4 (1999) 213-224
2. Sabino D., Costa L., Rizzatti E., and Zago M. A texture approach to leukocyte recognition. Real-Time Imaging 10 4 (2004) 205-216
3. Jalba A., Wilkinson M., and Roerdink J. Shape representation and recognition through morphological curvature scale spaces. IEEE Trans. Image Process. 15 2 (2006) 331-341
4. Trattner S., Greenspan H., Tepper G., and Abboud S. Automatic identification of bacterial types using statistical imaging methods. IEEE Trans. Med. Imaging 23 7 (2004) 807-820
5. Long X., Cleveland W., and Yao Y. Effective automatic recognition of cultured cells in bright field images using Fisher's linear discriminant preprocessing. Image Vision Comput. 23 13 (2005) 1203-1213
6. Sampat M., Bovik A., Aggarwal J., and Castleman K. Supervised parametric and non-parametric classification of chromosome images. Pattern Recognition 38 8 (2005) 1209-1223
7. Kucera J., and Reznicky M. Differentiation of species of Eimeria from the fowl using a computerized image-analysis system. Folia Parasitol. (Praha) 2 38 (1991) 107-113
8. Daugschies A., Imarom S., and Bollwahn W. Differentiation of porcine Eimeria spp. by morphologic algorithms. Vet. Parasitol. 81 3 (1999) 201-210
9. Plitt A., Imarom S., Joachim A., and Daugschies A. Interactive classification of porcine Eimeria spp. by computer-assisted image analysis. Vet. Parasitol. 86 2 (1999) 105-112
10. Long P.L., Millard B.J., Joyner L.P., and Norton C.C. A guide to laboratory techniques used in the study and diagnosis of avian coccidiosis. Folia Vet. Lat. 6 3 (1976) 201-217
11. Schnitzler B.E., Thebo P.L., Mattsson J.G., Tomley F.M., and Shirley M.W. Development of a diagnostic PCR assay for the detection and discrimination of four pathogenic Eimeria species of the chicken. Avian Pathol. 27 5 (1998) 490-497
12. Schnitzler B.E., Thebo P.L., Tomley F.M., Uggla A., and Shirley M.W. PCR identification of chicken Eimeria: a simplified read-out. Avian Pathol. 28 1 (1999) 89-93
13. Fernandez S., Pagotto A.H., Furtado M.M., Katsuyama A.M., Madeira A.M., and Gruber A. A multiplex PCR assay for the simultaneous detection and discrimination of the seven Eimeria species that infect domestic fowl. Parasitology 127 4 (2003) 317-325
14. Joachim A., Dulmer N., and Daugschies A. Differentiation of two Oesophagostomum spp. from pigs, O. dentatum and O. quadrispinulatum, by computer-assisted image analysis of fourth-stage larvae. Parasitol. Int. 48 1 (1999) 63-71
15. Sommer C. Quantitative characterization, classification and reconstruction of oocyst shapes of Eimeria species from cattle. Parasitology 116 1 (1998) 21-28
16. Sommer C. Quantitative characterization of texture used for identification of eggs of bovine parasitic nematodes. J. Helminthol. 72 2 (1998) 179-182
17. Yang Y., Park D., Kim H., Choi M., and Chai J. Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network. IEEE Trans. Biomed. Eng. 48 6 (2001) 718-730
18. Widmer K.W., Oshima K.H., and Pillai S.D. Identification of Cryptosporidium parvum oocysts by an artificial neural network approach. Appl. Environ. Microbiol. 68 3 (2002) 1115-1121
19. Bruno O., Cesar Jr. R., Consularo L., and Costa L. Automatic feature selection for biological shape classification in SYNERGOS. Proceedings of the SIBGRAPI'98, International Symposium on Computer Graphics, Image Processing, and Vision (1998) 363-370
20. Coelho R., Gesù V.D., Bosco G.L., Tanaka J., and Valenti C. Shape-based features for cat ganglion retinal cells classification. Real-Time Imaging 8 3 (2002) 213-226
21. Costa L., dos Reis S., Arantes R., Alves A., and Mutinari G. Biological shape analysis by digital curvature. Pattern Recognition 37 3 (2004) 515-524
22. Regan D. Human Perception of Objects (2000), York University, New York
23. Olshausen B., and Field D. Vision and the coding of natural images. Am. Sci. 88 3 (2000) 238-245
24. Zhang D., and Lu G. Review of shape representation and description techniques. Pattern Recognition 37 1 (2004) 1-19
25. Costa L., and Cesar Jr. R. Shape Analysis and Classification: Theory and Practice (2000), CRC Press, Boca Raton, FL
26. Gonzales R., and Woods R. Digital Image Processing (1993), Addison-Wesley, Reading, MA
27. Attneave F. Some informational aspects of visual perception. Psychol. Rev. 61 3 (1954) 183-193
28. Riggs L. Curvature as a feature of pattern vision. Science 181 4104 (1973) 1070-1072
29. Mokhtarian F., and Mackworth A. A theory of multiscale, curvature-based shape representation for planar curves. IEEE Trans. Pattern Anal. Mach. Intell. 14 8 (1992) 789-805
30. Cesar Jr. R., and Costa L. Towards effective planar shape representation with multiscale digital curvature analysis based on signal processing techniques. Pattern Recognition 29 9 (1996) 1559-1569
31. Weyl H. Symmetry (1980), Princeton University Press, New Jersey
32. Zabrodsky H., Peleg S., and Avnir D. A measure of symmetry based on shape similarity. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR92) (1992) 703-706
33. M. Brady, H. Asada, Smoothed local symmetries and their implementation, Technical Report, Cambridge, MA, USA, 1984.
34. Sato J., and Cipolla R. Affine integral invariants for extracting symmetry axes. Image Vision Comput. 15 8 (1997) 627-635
35. Bonneh Y., Reisfeld D., and Yeshurun Y. Quantification of local symmetry: application to texture discrimination. Spat. Vision 8 4 (1994) 515-530
36. Zavidovique B., and Gesù V.D. Kernel based symmetry measure. ICIAP (2005) 261-268
37. Young I., Walker J., and Bowie J. An analysis technique for biological shape I. Inform. Control 25 4 (1974) 357-370
38. Tuceryan M., and Jain A. Texture analysis. In: Chen C.H., Pau L.F., and Wang P.S.P. (Eds). The Handbook of Pattern Recognition and Computer Vision. second ed. (1998), World Scientific Publishing Co., Singapore 207-247
39. Haralick R., Shanmugam K., and Dinstein I. Textural features for image classification. IEEE Trans. Systems Man Cybern. SMC-3 6 (1973) 610-621
40. Jobanputra R., and Clausi D. Preserving boundaries for image texture segmentation using grey level co-occurring probabilities. Pattern Recognition 39 2 (2006) 234-245
41. Conners R. Towards a set of statistical features which measure visually perceivable qualities of texture. Proceedings of Pattern Recognition Image Processing Conference (1979) 382-390
42. Theodoridis S., and Koutroumbas K. Pattern Recognition (1998), Academic Press, San Diego
43. Duda R., Hart P., and Stork D. Pattern Classification (2001), Wiley, New York
44. Narendra P.M., and Fukunaga K. A branch and bound algorithm for feature subset selection. IEEE Trans. Comput. 26 9 (1977) 917-922
45. Jain A., Duin R., and Mao J. Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22 1 (2000) 4-37
46. Jain A., and Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19 2 (1997) 153-158
47. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (2000), Cambridge University Press, Cambridge
48. Crammer K., and Singer Y. On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2 5 (2001) 265-292
49. Barta J.R., Martin D.S., Liberator P.A., Dashkevicz M., Anderson J.W., Feighner S.D., Elbrecht A., Perkins-Barrow A., Jenkins M.C., Danforth H.D., Ruff M.D., and Profous-Juchelka H. Phylogenetic relationships among eight Eimeria species infecting domestic fowl inferred using complete small subunit ribosomal DNA sequences. Parasitology 83 2 (1997) 262-271