Automatic recognition of five types of white blood cells in peripheral blood

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

Volumn 6112 LNCS, Issue PART 2, 2010, Pages 161-172

  Rezatofighi, Seyed Hamid ^a   Khaksari, Kosar ^a   Soltanian Zadeh, Hamid ^a,b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b HENRY FORD HOSPITAL   (United States)

Author keywords

classification; feature selection; peripheral blood; segmentation; textural feature; White blood cell

Indexed keywords

AUTOMATIC RECOGNITION; AUTOMATIC SYSTEMS; CLASSIFICATION FEATURES; CO-OCCURRENCE-MATRIX; GRAM-SCHMIDT ORTHOGONALIZATIONS; IMAGE PROCESSING TECHNIQUE; LOCAL BINARY PATTERNS; NEW SYSTEM; PERIPHERAL BLOOD; SEQUENTIAL FORWARD SELECTION; SNAKE ALGORITHM; TEXTURAL FEATURE; WHITE BLOOD CELLS;

ABERRATIONS; CELLS; CLASSIFIERS; CYTOLOGY; DIAGNOSIS; FEATURE EXTRACTION; IMAGE ANALYSIS;

BLOOD;

EID: 77955411482     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13775-4_17     Document Type: Conference Paper

References (23)

1. Sabino, D.M.U., Costa, L.D.F., Rizzatti, E.G., Zago, M.A.: A Texture Approach to Leukocyte Recognition. Real-Time Imaging 10(4), 205-216 (2004)
2. Angulo, J., Flandrin, G.: Automated Detection of Working Area of Peripheral Blood Smears Using Mathematical Morphology. Anal. Cell Pathol. 25, 37-49 (2003)
3. Wu, J., Zeng, P., Zhou, Y., Oliver, C.: A Novel Color Image Segmentation Method and Its Application to White Blood Cell Image Analysis. In: 8th International Conference on Signal Processing (ICSP'06), vol. 2, pp. 245-248. IEEE Press, Beijng (2006)
4. Umpon, N.T.: Patch-based White Blood Cell Nucleus Segmentation Using Fuzzy Clustering. ECTI Trans. Electrical Electronic Communications 3(1), 5-10 (2005)
5. Guo, N., Zeng, L., Wu, Q.: A Method Based on Multi-Spectral Imaging Technique for White Blood Cell Segmentation. Comput. Biol. Med. 37(1), 70-76 (2006)
6. Chassery, J.M., Garbay, C.: An Iterative Segmentation Method Based on Contextual Color and Shape Criterion. IEEE Trans. Pattern Anal. Machine Intell. 6(6), 795-800 (1984)
7. Jiang, K., Liao, Q.M., Dai, S.Y.: A Novel White Blood Cell Segmentation Scheme Using Scale-Space Filtering and Watershed Clustering. In: 2nd International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2820-2825 (2003)
8. Zamani, F., Safabakhsh, R.: An Unsupervised GVF Snake Approach for White Blood Cell Segmentation Based on Nucleus. In: 8th International Conference on Signal Processing (ICSP'06), vol. 2. IEEE Press, Beijng (2006)
9. Park, J., Keller, J.M.: Snakes on the Watershed. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1201-1205 (2001)
10. Umpon, N.T., Dhompongsa, S.: Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification. IEEE Trans. Inf. Technol. Biomed. 11(3), 353-359 (2007)
11. Thiran, J.P., Macq, B.: Morphological Feature Extraction for the Classification of Digital Images of Cancerous Tissues. IEEE Trans. Bio. Med. Eng. 43(10), 1011-1020 (1996)
12. Hengen, H., Spoor, S.L., Pandit, M.C.: Analysis of Blood and Bone Marrow Smears Using Digital Image Processing Techniques. In: Sonka, M., Fitzpatrick, J.M. (eds.) Proceedings of the SPIE, Medical Imaging 2002: Image Processing, vol. 4684, pp. 624-635 (2002)
13. Umpon, N.T., Gader, P.D.: System-Level Training of Neural Networks for Counting White Blood Cells. IEEE Trans. Syst., Man, Cybern. 32(1), 48-53 (2002)
14. Long, X., Cleveland, W.L., Yao, Y.L.: A New Preprocessing Approach for Cell Recognition. IEEE Trans. Inf. Technol. Biomed. 9(3), 407-412 (2005)
15. Nattkemper, T.W., Ritter, H.J., Schubert, W.: A Neural Classifier Enabling High-Throughput Topological Analysis of Lymphocytes in Tissue Sections. IEEE Trans. Inf. Technol. Biomed. 5(2), 138-149 (2001)
16. Shitong, W., Min, W.: A New Detection Algorithm (NDA) Based on Fuzzy Cellular Neural Networks for White Blood Cell Detection. IEEE Trans. Inf. Technol. Biomed. 10(1), 5-10 (2006)
17. Rezatofighi, S.H., Soltanian-Zadeh, H., Sharifian, R., Zoroofi, R.A.: A New Approach to White Blood Cell Nucleus Segmentation Based on Gram-Schmidt Orthogonalization. In: 2nd International Conference on Digital Image Processing (ICDIP'09), pp. 107-111. IEEE Press, Thailand (2009)
18. Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cyber. 3(6), 610-621 (1973)
19. Ojala, T., PietikaÈ inen, M., MaÈenpa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Machine Intell. 24(7), 971-987 (2002)
20. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. of Comput. Vision 1(4), 321-331 (1988)
21. Pudil, P., Novovičová, J., Kittler, J.: Floating Search Methods in Feature Selection. Pattern Recogn. Lett. 15(11), 1119-1125 (1994)
22. Basheer, I.A., Hajmeer, M.: Artificial Neural Networks: Fundamentals, Computing, Design, and Application. J. Microbiol. Meth. 43(1), 3-31 (2000)
23. Burges, C.J.C.: A tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining 2(2), 121-167 (1998)