A fluid dynamics-based deformable model for segmentation of cervical cell images

Signal, Image and Video Processing

Volumn 8, Issue 1, 2014, Pages 21-32

  Cengizler, Caglar ^a   Guven, Mustafa ^a   Avci, Mutlu ^a  

^a CUKUROVA UNIVERSITY   (Turkey)

Author keywords

Deformable; Fluid dynamics; Pap smear; Particles; Segmentation

Indexed keywords

CELLS; CYTOLOGY; DEFORMATION; ELEMENTARY PARTICLES; FLUID DYNAMICS; FLUID MECHANICS; NAVIER STOKES EQUATIONS; TOPOLOGY;

CYTOPLASMIC REGIONS; DEFORMABLE; DEFORMABLE MODELING; INCOMPRESSIBLE FLUID; INTERACTION MECHANISMS; PAP SMEAR; UNSUPERVISED SEGMENTATION; WATER-SHED ALGORITHM;

IMAGE SEGMENTATION;

EID: 84925539463     PISSN: 18631703     EISSN: 18631711     Source Type: Journal    
DOI: 10.1007/s11760-014-0719-3     Document Type: Article

References (17)

1. Chen, Y., Huang, P., Lin, K., Wang, L., Lin, H., Cheng, C., Chen, T., Chan, Y., Chiang, J.: Semi-automatic segmentation and classification of pap smear cells. IEEE J. Biomed. Health Inf. 18(1), 94–108 (2014)
2. Papanicolaou, G.N.: A new procedure for staining vaginal smears. Science 95, 438–439 (1942)
3. Dougherty, D.: Medical Image Processing Techniques and Applications. Springer, New York (2011)
4. Plissiti, M.E., Nikou, C., Charchanti, A.: Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern. Recogn. Lett. 32(6), 838–853 (2011)
5. Wu, H.-S., Barba, J., Gil, J.: A parametric fitting algorithm for segmentation of cell images. IEEE Trans. Biomed. Eng. 45(3), 400–407 (1998)
6. Tsai, M.-H., Chan, Y.-K., Lin, Z.-Z., Yang-Mao, S.-F., Huang, P.-C.: Nucleus and cytoplast contour detector of cervical smear image. Pattern. Recogn. Lett. 29(9), 1441–1453 (2008)
7. Plissiti, M.E., Nikou, C., Charchanti, A.: Watershed-based segmentation of cell nuclei boundaries in Pap smear images. In: Proceedings of IEEE International Conference on Information Technology and Applications in Biomedicine, pp. 1–4 (2010)
8. Xu, C., Prince, J.L.: Generalized gradient vector flow external forces for active contours. Signal Process 71(2), 131–139 (1998)
9. Chan, T.F., Vese, L.A.: Active contours without edges. In: Proceedings of IEEE International Conference on Image Processing, pp. 266–277 (2001)
10. Williams, D.J., Shah, M.: A fast algorithm for active contours and curvature estimation. CVGIP Imag. Underst. 55(1), 14–26 (1992)
11. Bamford, P., Lovell, B.: Unsupervised cell nucleus segmentation with active contours. Signal Process 71(2), 203–213 (1998)
12. Floyd, R.W.: An adaptive algorithm for spatial gray-scale. Proc. Soc. Inf. Disp. 17, 75–77 (1976)
13. Cardon Cline, D., Egbert, P.K.: Fluid flow for the rest of us: tutorial of the marker and cell method in computer graphics (2014). http://www.proxyarch.com/util/techpapers/papers/Fluidflowfortherestofus
14. Stam, J.: Real-time fluid dynamics for games. In: Proceedings of the Game Developer Conference, pp. 18–25 (2003)
15. Jalba, A.C., Roerdink, J.B.T.M.: A physically-motivated deformable model based on fluid dynamics. In: Computer Vision – ECCV 2006, pp. 496–507. Springer, Heidelberg (2006)
16. Lipkus, A.H.: A proof of the triangle inequality for the Tanimoto distance. J. Math. Chem. 26(1–3), 263–265 (1999)
17. Kurugollu, F., Sankur, B., Harmanci, A. E.: Color image segmentation using histogram multithresholding and fusion. Image Vis. Comput. 19(13), 915–928 (2001)