Classification of spatial textures in benign and cancerous glandular tissues by stereology and stochastic geometry using artificial neural networks

Journal of Microscopy

Volumn 198, Issue 2, 2000, Pages 143-158

  Mattfeldt, T ^a,b   Gottfried, H W ^a   Schmidt, V ^a   Kestler, H A ^a  

^a UNIVERSITY OF ULM   (Germany)

^b Department of Pathology ^*  (Germany)

Author keywords

Artificial intelligence; Boolean model; Cancer; Classification; Covariance function; Discriminant analysis; Neural networks; Pair correlation function; Pathology; Prostatic neoplasms; Stereology; Stochastic geometry

Indexed keywords

BACKPROPAGATION; DISCRIMINANT ANALYSIS; DISEASES; GEOMETRY; HISTOLOGY; MULTILAYERS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TISSUE; VECTOR QUANTIZATION;

BOOLEAN MODELING; CANCER; COVARIANCE FUNCTION; FIRST ORDER; GLANDULAR TISSUE; NEURAL-NETWORKS; PAIR CORRELATION FUNCTIONS; PROSTATIC NEOPLASMS; STEREOLOGY; STOCHASTIC GEOMETRY;

PATHOLOGY;

ACCURACY; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; HUMAN; IMAGE ANALYSIS; IMAGE QUALITY; IMAGE RECONSTRUCTION; PRIORITY JOURNAL; STEREOLOGY; STOCHASTIC MODEL; TUMOR BIOPSY;

EID: 0034111340     PISSN: 00222720     EISSN: None     Source Type: Journal    
DOI: 10.1046/j.1365-2818.2000.00689.x     Document Type: Article

References (46)

1. Armitage, P. & Berry, G. (1994) Statistical Methods in Medical Research. Blackwell, Oxford.
2. Bindrich, U. & Stoyan, D. (1991) Stereology of the pore structure in bread. J. Microsc. 162, 231-239.
3. Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984) Classification and Regression Trees. Wadsworth, Belmont.
4. Conrad, S., Graefen, M., Pichlmeier, U., Henke, R.-P., Hammerer, P.G. & Huland, H. (1998) Systemic sextant biopsies improve preoperative prediction of pelvic lymph node metastases in patients with clinically localized prostatic carcinoma. J. Urol. 159, 2023-2029.
5. Cruz-Orive, L.-M. (1989) Second-order stereology: estimation of second moment volume measures. Acta Stereol. 8, 641-646.
6. Einstein, A.J., Barba, J., Unger, P.D. & Gil, J. (1994) Nuclear diffusencss as a measure of texture: definition and application to the computer-assisted diagnosis of parathyroid adenoma and carcinoma. J. Microsc. 176, 158-166.
7. Erler, B.S., Hsu, L., Truong, H.M., Petrovic, L.M., Kim, S.S., Huh, M.H., Ferrell, L.D., Thung, S.N., Geller, S.A. & Marchevsky, A.M. (1994) Image analysis and diagnostic classification of hepatocellular carcinoma using neural networks and multivariate discriminant functions. Lab. Invest. 71, 446-451.
8. Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. 2nd edn. Academic Press, San Diego.
9. Gerlach, W. & Stoyan, D. (1986) Determination of the covariance of random sets from thin sections. Biometr. J. 4, 427-432.
10. Hanley, J.A. & McNeil, B.J. (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36.
11. Henderson, A.R. (1993) Assessing test accuracy and its clinical consequences: a primer for receiver operating characteristic curve analysis. Ann. Clin. Biochem. 30, 521-539.
12. Howard, C.V. & Reed, M.G. (1998) Unbiased Stereology. Three-Dimensional Measurement in Microscopy. Bios Scientific Publishers, Oxford.
13. König, D. & Stoyan, D. (1986) Second-order moments in stereological study of random forms. Sci. Form, 2, 21-35.
14. König, D. & Schmidt, V. (1992) Zufällige Punktprozesse. Teubner, Stuttgart.
15. Kohonen, T. (1997) Self-Organizing Maps. 2nd edn. Springer, Heidelberg.
16. Lang, T.A. & Secic, M. (1997) How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors and Reviewers. American College of Physicians/BMJ Publishing Group. Philadelphia.
17. Markopoulos, C., Karakitsos, P., Botsoli-Stergiou, E., Pouliakis, A., Iokim-Liossi, A., Kyrkou, K. & Gogas, J. (1997) Application of the learning vector quantizer to the classification of breast lesions. Analyt. Quant. Cytol. Histol. 19, 453-460.
18. Mattfeldt, T., Frey, H. & Rose, C. (1993a) Second-order stereology of benign and malignant alterations of the human mammary gland. J. Microsc. 171, 143-151.
19. Mattfeldt, T., Vogel, U., Gottfried, H.-W. & Frey, H. (1993b) Second-order stereology of prostatic adenocarcinoma and normal prostatic tissue. Acta Stereol. 12, 203-208.
20. Mattfeldt, T., Schmidt, V., Reepschläger, D., Rose, C. & Frey, H. (1996) Centred contact density functions for the statistical analysis of random sets. A stereological study on benign and malignant glandular tissue using image analysis. J. Microsc. 183, 158-169.
21. Mattfeldt, T., Kestler, H.A., Hautmann, R. & Gottfried, H.-W. (1999) Prediction of prostatic cancer progression after radical prostatectomy using artificial neural networks: a feasibility study. Br. J. Urol. 84, 316-323.
22. Mayhew, T.M. (1999a) Quantitative description of the spatial arrangement of organelles in a polarised secretory epithelial cell: the salivary gland acinar cell. J. Anat. 194, 279-285.
23. Mayhew, T.M. (1999b) Second-order stereology and ultrastructural examination of the spatial arrangements of tissue compartments within glomeruli of normal and diabetic kidneys. J. Microsc. 195, 87-95.
24. Molchanov, I. (1997) Statistics of the Boolean Model for Practitioners and Mathematicians. Wiley, Chichester.
25. Nafe, R. & Choritz, H. (1992) Introduction of a neuronal network as a tool for diagnostic analysis and classification based on experimental pathologic data. Exp. Toxic. Pathol. 44, 17-24.
26. Ohser, J. (1980) On statistical analysis of the Boolean model. Elektron. Informationsverarb. Kybern. 16, 651-653.
27. Ohser, J. (1991) On estimation of pair correlation functions. Res. Inform. 4, 147-152.
28. Pantazopoulos, D., Karakitsos, P., Iokim-Liossi, A., Pouliakis, A. & Dimopoulos, K. (1998a) Comparing neural networks in the discrimination of benign from malignant lower urinary tract lesions. Br. J. Urol. 81, 574-579.
29. Pantazopoulos, D., Karakitsos, P., Pouliakis, A., Iokim-Liossi, A. & Dimopoulos, M.A. (1998b) Static cytometry and neural networks in the discrimination of lower urinary system lesions. Urology, 51, 946-950.
30. Reed, M.G. & Howard, C.V. (1999) Stereological estimation of covariance using linear dipole probes. J. Microsc. 195, 96-103.
31. Riedmiller, M. & Braun, H. (1992) Rprop - a Fast Adaptive Learning Algorithm. Technical Report, University of Karlsruhe, Germany.
32. Riedmiller, M. & Braun, H. (1993) A direct adaptive method for faster backpropagation learning. The RPROP algorithm. IEEE International Conference on Neural Networks, San Francisco, pp. 586-591.
33. SAS Institute (1988) SAS/STAT User's Guide. Release 6.03 Edition. SAS Institute Inc, Cary, NC.
34. Serra, J. (1980) The Boolean model and random sets. Computer Graph. Image Process. 12, 99-126.
35. Serra, J. (1982) Image Analysis and Mathematical Morphology. Academic Press, London.
36. Sokal, R.R. & Rohlf, F.J. (1981) Biometry. 2nd edn. Freeman, San Francisco.
37. Stoyan, D., Kendall, W.S. & Mecke, J. (1995) Stochastic Geometry and its Applications. 2nd edn. Wiley, Chichester.
38. Swets, J.A. & Pickett, R.M. (1982) Evaluation of Diagnostic Systems. Academic Press, New York.
39. Tewari, A. (1997) Artificial intelligence and neural networks: concept, applications and future in oncology. Br. J. Urol. 80 (Suppl. 3), 53-58.
40. Therrien, C.W. (1989) Decision Estimation and Classification. Wiley, New York.
41. Tourassi, G.D. & Floyd, C.E. (1997) The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis. Med. Decis. Making, 17, 186-192.
42. Vapnik, V.N. (1998) Statistical Learning Theory. Wiley, New York.
43. Wei, J.T., Zhang, Z., Barnhill, S.D., Madyastha, K.R., Zhang, H. & Oesterling, J.E. (1998) Understanding artificial neural networks and exploring their potential applications for the practicing urologist. Urology, 52, 161-172.
44. Weibel, E.R. (1979) Stereological Methods. I. Practical Methods for Biological Morphometry. Academic Press, London.
45. Wilding, P., Morgan, M.A., Grygotis, A.E., Shoffner, M.A. & Rosato, E.F. (1994) Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. Cancer Lett. 77, 145-153.
46. Zell, A. (1994) Simulation Neuronaler Netze. Addison-Wesley, Bonn.