Leukocyte segmentation in blood smear images using region-based active contours

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

Volumn 4179 LNCS, Issue , 2006, Pages 867-876

  Eom, Seongeun ^a   Kim, Seungjun ^a   Shin, Vladimir ^a   Ahn, Byungha ^a  

^a Gwangju Institute of Science and Technology (GIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD; CYTOLOGY; IMAGE ANALYSIS; IMAGING SYSTEMS; MATHEMATICAL MODELS; STATISTICS;

CONTOURS; LEUKOCYTES; WHITE BLOOD CELLS;

IMAGE SEGMENTATION;

EID: 33750256515     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11864349_79     Document Type: Conference Paper

References (19)

1. Wermser, D., Haussmann, G., Liedtke, C.E.: Segmentation of blood smears by hierarchical thresholding. Computer Vision, Graphics, and Image Processing 25 (1984) 151-168
2. Cseke, I.: A fast segmentation scheme for white blood cell images. In: Proc. 11th IAPR Int. Conf. Pattern Recognition, Conf. C: Image, Speech and Signal Analysis. Volume 3. (1992) 530-533
3. Sinha, N., Ramakrishnan, A.G.: Automation of differential blood count. In: Proc. Conf. Convergent Technologies for Asia-Pacific Region TENCON 2003. Volume 2. (2003) 547-551
4. Haussmann, G., Liedtke, C.E.: A region extraction approach to blood smear segmentation. Computer Vision, Graphics, and Image Processing 25 (1984) 133-150
5. Park, J., Keller, J.M.: Fuzzy patch label relaxation in bone marrow cell segmentation. In: Proc. IEEE Int. Conf. Systems, Man, and Cybernetics. (1997) 1133-1138
6. Ongun, G., Halici, U., Leblebicioglu, K., Atalay, V., Beksac, M., Beksac, S.: An automated differential blood count system. In: Proc. 23rd EMBS Int. Conf. (2001) 2583-2586
7. Nilsson, B., Heyden, A.: Segmentation of dense leukocyte clusters. In: Proc. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. (2001) 221-227
8. Nilsson, B., Heyden, A.: Model-based segmentation of leukocytes clusters. In: Proc. 16th International Conf. Pattern Recognition. Volume 1. (2002) 727-730
9. Theerapattanakul, J., Plodpai, J., Pintavirooj, C.: An efficient method for segmentation step of automated white blood cell classifications. In: Proc. IEEE Region 10 Conf. TENCON 2004. Volume 1. (2004) 191-194
10. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. 2nd edn. Wiley-Interscience, New York (2000)
11. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42 (1989) 577-684
12. (http://www.cellatlas.com)
13. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Computer Vision 1 (1988) 321-331
14. Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. 1st edn. Springer, New York (2002)
15. Sethian, J.A.: Level Set Methods and Fast Marching Methods. 2nd edn. Cambridge University Press, Cambridge (1999)
16. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2) (2001) 266-277
17. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3) (1998) 359-369
18. Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing. Volume 147 of Applied Mathematical Sciences. Springer, New York (2001)
19. Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in mr images: method and validation. IEEE Trans. Medical Imaging 13(4) (1994) 716-724