Mining knowledge for HEp-2 cell image classification

Artificial Intelligence in Medicine

Volumn 26, Issue 1-2, 2002, Pages 161-173

  Perner, Petra ^a   Perner, Horst ^a   Müller, Bernd ^a  

^a Inst Comp Vis/Appl Comp Sci   (Germany)

Author keywords

Data mining; Decision tree induction; Fluorescence image analysis; HEp 2 cell classification; Image mining; Medical diagnosis; Texture classification

Indexed keywords

ALGORITHMS; ANTIBODIES; CELLS; DATA ACQUISITION; DATA MINING; IMAGE ANALYSIS; MICROSCOPES;

CROSS VALIDATIONS;

ARTIFICIAL INTELLIGENCE;

ALGORITHM; ARTICLE; CELL ASSAY; CONTROLLED STUDY; EXPERT SYSTEM; HEP 2 CELL; HUMAN; HUMAN CELL; IMAGE ANALYSIS; MEDICAL INFORMATICS; PRIORITY JOURNAL; VALIDATION PROCESS;

ALGORITHMS; ANTIBODIES, ANTINUCLEAR; CARCINOMA, HEPATOCELLULAR; DECISION TREES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; LIVER NEOPLASMS; REPRODUCIBILITY OF RESULTS; TUMOR CELLS, CULTURED;

EID: 0036754439     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0933-3657(02)00057-X     Document Type: Article

References (16)

1. Abe Y., Kimura K., Horiuchi A., Miyake M., Kimura Sh. Improvement of ELISA sensitivity by allogeneric adsorption of polyclonal antibodies: a technical note for non-experts. Clin. Chim. Acta. 224:1994;103-105.
2. Baird HS, Mallows CL. Bounded-error in pre-classification trees. In: Dori D, Bruckstein A, editors. Shape, Structure and Pattern Recognition. Singapore: World Scientific, 1995, p. 100-10.
3. Bradwell AR, Stokes RP, Johnson GD. Atlas of HEp-2 Patterns. KNP Group Ltd., 1995.
4. Cios KJ, Teresinska A, St. Konieczna, Potocka J, Sharma S. Diagnosing myocardial perfusion SPECT bull's-eye maps: A knowledge discovery approach. IEEE Eng. Med. Biol. Mag. 19(4);17-25.
5. Conrad K, Humbel R-L, Meurer M, Shoenfeld T, Tan EM. editors. Autoantigens and Autoantibodies: Diagnostic Tools and Clues to Understanding Autoimmunity. Berlin: Pabst Science Publisher, 2000.
6. Garcia P., Petrou M., Kamata S. The use of the Boolean model for texture analysis of gray images. Comput. Vision Image Understand. 74:1999;227-235.
7. Matheron G. Random Sets and Integral Geometry. New York: Wiley, 1975.
8. Nakabayashi I, et al., Evaluation of the automatic fluorescent image analysis, image titer, for quantitative analysis of antinuclear antibodies. Am J Clin Pathol 2001:115. Online: http://www.ajcp.com/.
9. Niemann H. Pattern Analysis and Understanding. Berlin (Heideberg): Springer, 1990.
10. Otsu N. A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybernet. 9:1979;38-52.
11. Perner P, Trautzsch S. Multi-interval discretization for decision tree learning. In: Amin A, Dori D, Pudil P, Freeman H, editors. Advances in pattern recognition, LNCS 1451. Berlin (Heidelberg): Springer, 1998. p. 475-82.
12. Perner P. A knowledge-based image inspection system for automatic defect recognition, classification, and process diagnosis. Int. J. Machine Vision Appl. 7:1994;135-147.
13. Petrou M, Bosdogianni P. Image Processing: The Fundamentals. Chichester: Wiley, 1999.
14. Quinlain J.R. Simplifying decision tree. Int. J. Man-Machine Stud. 27:1987;221-234.
15. Stoyan D, Kendall WS, Mecke J. Stochastic Geometry and its Applications. Berlin: Akademie Verlag, 1987.
16. Weiss SM, Kulikowski CA. Computer Systems that Learn: Classification And Prediction Methods from Statistics, Neural Networks, Machine Learning, and Expert Systems. San Mateo: Morgan Kaufmann, 1990.