Spermatogonium image recognition using Zernike moments

Computer Methods and Programs in Biomedicine

Volumn 95, Issue 1, 2009, Pages 10-22

  Liyun, Wang ^a   Hefei, Ling ^a   Fuhao, Zou ^a   Zhengding, Lu ^a   Zhendi, Wang ^b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Cell recognition; Mathematical morphology; Zernike moments

Indexed keywords

AUTOMATIC IDENTIFICATIONS; CELL IMAGES; CELL RECOGNITION; EUCLIDEAN DISTANCES; HU MOMENTS; IMAGE FEATURES; INDIVIDUAL CELLS; NORMALIZED IMAGES; ZERNIKE MOMENTS;

AUTOMATION; BIOMEDICAL ENGINEERING; ELECTRONIC DATA INTERCHANGE; ENGINEERING RESEARCH; FACE RECOGNITION; MATHEMATICAL MORPHOLOGY;

METHOD OF MOMENTS;

ARTICLE; ELECTRON MICROSCOPY; IMAGE ANALYSIS; MATHEMATICAL ANALYSIS; SPERMATOGONIUM;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; AUTOMATION; CELL SEPARATION; COMPUTATIONAL BIOLOGY; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MICROSCOPY, ELECTRON; MODELS, STATISTICAL; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; RATS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SPERMATOGONIA;

EID: 64749110697     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2009.01.008     Document Type: Article

References (19)

1. Fok Y.L., Chan J.C.K., and Chin R.T. Automated analysis of nerve-cell images using active contour models. IEEE Transactions on Medical Imaging 15 3 (1996) 353-368
2. Shigetoshi S., et al. Cell recognition by image processing: (recognition of dead or living plant cells by neural network). JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing 37 1 (1994) 202-208
3. Kovalev V.A., Grigoriev A.Y., Ahn H.-S., and Myshkin N.K. Segmentation technique of complex image scene for an automatic blood cell counting system. In: Loew M.H., and Hanson K.M. (Eds). Medical Imaging 1996: Image Processing (1996), SPIE, Newport Beach, CA 805-810
4. Loukas C.G., Wilson G.D., Vojnovic B., and Linney A. An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections. Cytometry 55A (2003) 30-42
5. Nattkemper T.W., Twellmann T., Ritter H., and Schubert W. Human vs. machine: evaluation of fluorescence micrographs. Computers in Biology and Medicine 33 (2003) 31-43
6. Nattkemper T.W., Ritter H., and Schubert W. A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections. IEEE Transactions on Information Techniques Biomedicine 5 2 (2001) 138-149
7. Nattkemper T.W., Wersing H., Schubert W., and Ritter H. A neural network architecture for automatic segmentation of fluorescence micrographs. Neurocomputing 48 (2002) 357-367
8. Long X., Cleveland W.L., and Yao Y.L. Effective automatic recognition of cultured cells in bright field images using Fisher's linear discriminant preprocessing. Image and Vision Computing 23 13 (2005) 1203-1213
9. Zhenjie H., Shuoshi M., Xichun P., and Xin P. Study on recognition of marrow cell image based on morphology and neural networks. Computer Engineering 32 11 (2006) 203-205
10. Long X., Cleveland W.L., and Yao Y.L. A new preprocessing approach for cell recognition. IEEE Transactions on Information Technology in Biomedicine 9 3 (2005) 407-412
11. Ushizima D.M., Lorena A.C., and de Carvalho A.C.P.L.F. Support vector machines applied to white blood cell recognition. Proceeings of the Fifth International Conference on Hybrid Intelligent Systems. IEEE, Rio de Janeiro, Brazil (2005) 379-384
12. Siroic R., Osowski S., Markiewicz T., and Siwek K. Support vector machine and genetic algorithm for efficient blood cell recognition. Instrumentation and Measurement Technology Conference Proceedings. IEEE, Warsaw, Poland (2007) 1-6
13. Ren H.P., Ping Z.L., et al. Cell image recognition with radial harmonic Fourier moments. Chinese Physics 12 6 (2003) 610-614
14. Hu M.K. Visual pattern recognition by moment invariants. IEEE Transaction on Information Theory 8 2 (1962) 179-187
15. Teague M.R. Image analysis via the general theory of moments. Journal of the Optical Society of America 70 8 (1980) 920-930
16. Teh C.H., and Chin R.T. On image analysis by the methods of moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 10 4 (1988) 496-513
17. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9 1 (1979) 62-66
18. Chong C.W., Raveendran P., and Mukundan R. A comparative analysis of algorithms for fast computation for Zernike moments. Pattern Recognition 36 3 (2003) 731-741
19. Khotanzad A., and Hong Y.H. Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 5 (1990) 489-497