Fractal characterization of chromatin appearance for diagnosis in breast cytology

Journal of Pathology

Volumn 185, Issue 4, 1998, Pages 366-381

  Einstein, Andrew J ^a   Wu, Hai Shan ^a   Sanchez, Miguel ^b   Gil, Joan ^a  

^a MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^b ENGLEWOOD HOSPITAL AND MEDICAL CENTER   (United States)

Author keywords

Artificial neural networks; Breast cytology; Fractal dimension; Fractals; Image analysis; Lacunarity; Logistic regression; Morphometry

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; ASPIRATION BIOPSY; BREAST BIOPSY; BREAST CARCINOMA; BREAST DISEASE; CHROMATIN; CHROMATIN STRUCTURE; CYTOLOGY; FRACTAL ANALYSIS; HUMAN; HUMAN CELL; IMAGE ANALYSIS; MORPHOMETRICS; PRIORITY JOURNAL; STATISTICAL MODEL;

BIOPSY, NEEDLE; BREAST; BREAST NEOPLASMS; CARCINOMA, DUCTAL, BREAST; CHROMATIN; EPITHELIAL CELLS; FEMALE; FRACTALS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER);

EID: 0031870752     PISSN: 00223417     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1096-9896(199808)185:4<366::AID-PATH122>3.0.CO;2-C     Document Type: Article

References (72)

1. Doudkine A, Macaulay C, Poulin N, Palcic B. Nuclear texture measurements in image cytometry. Pathologica 1995; 87: 286-299.
2. Haralick RM, Shanumgam K, Dinstein I. Textual features for image classification. IEEE Trans Syst Man Cybern 1973; SMC-3: 610-621.
3. Pressman NJ. Markovian analysis of cervical cell images. J Histochem Cytochem 1976; 24: 138-144.
4. Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process 1975; 4: 172-179.
5. Julesz B. Textons, the elements of texture perception, and their interactions. Nature 1981; 290: 91-97.
6. Brigham E. Fast Fourier Transform and Its Applications. Englewood Cliffs, NJ: Prentice Hall, 1988.
7. Serra J. Image Analysis and Mathematical Morphology. London: Academic Press, 1982.
8. Einstein AJ, Barba J, Unger PD, Gil J. Nuclear diffuseness as a measure of texture: definition and application to the computer-assisted diagnosis of parathyroid adenoma and carcinoma. J Microsc 1994; 176: 158-166.
9. Diegenbach PC, Baak JPA. Quantitative nuclear image analysis: differentiation between normal, hyperplastic, and malignant appearing uterine glands in a paraffin section. III. The use of texture features for differentiation. Eur J Obstet Gynecol Reprod Biol 1978; 8: 109-116.
10. Beil M, Irinopoulou T, Vassy J, Rigaut JP. Chromatin texture analysis in three-dimensional images from confocal scanning laser microscopy. Anal Quant Cytol Histol 1995; 17: 323-331.
11. Mandelbrot BB. The Fractal Geometry of Nature. New York: W. H. Freeman, 1983.
12. Cross SS. Fractals in pathology. J Pathol 1997; 182: 1-8.
13. Losa GA, Nonnenmacher TF. Self-similarity and fractal irregularity in pathologic tissues. Mod Pathol 1996; 9: 174-182.
14. Losa GA, Baumann G, Nonnenmacher TF. Fractal dimension of pericellular membranes in human lymphocytes and lymphoblastic leukemia cells. Pathol Res Pract 1992; 188: 680-686.
15. Smith TG Jr, Marks WB, Lange GD, Sheriff WH Jr, Neale EA. A fractal analysis of cell images. J Neurosci Methods 1989; 27: 173-180.
16. Landini G, Rippin JW. An 'asymptotic fractal' approach to the morphology of malignant cell nuclei. Fractals 1993; 1: 326-335.
17. Cross SS, Bury JP, Silcocks PB, Stephenson TJ, Cotton DW. Fractal geometric analysis of colorectal polyps. J Pathol 1994; 172: 317-323.
18. Landini G, Rippin JW. How important is tumour shape? Quantification of the epithelial-connective tissue interface in oral lesions using local connected fractal dimension analysis. J Pathol 1996; 179: 210-217.
19. Fazzalari NL, Parkinson IH. Fractal dimension and architecture of trabecular bone. J Pathol 1996; 178: 100-105.
20. Vilela MJ, Martins ML, Boschetti SR. Fractal patterns for cells in culture. J Pathol 1995; 177: 103-107.
21. Matsuyama T, Matsushita M. Fractal morphogenesis by a bacterial cell population. Crit Rev Microbiol 1993; 19: 117-135.
22. Yang GC, Alvarez II. Ultrafast Papanicolaou stain. An alternative preparation for fine needle aspiration cytology. Acta Cytol 1995; 39: 55-60.
23. Einstein AJ, Gil J, Wallenstein S, et al. Reproducibility and accuracy of interactive segmentation procedures for image analysis in cytology. J Microsc 1997; 188: 136-148.
24. Russ JC. Fractal Surfaces. New York: Plenum Press, 1994.
25. Mandelbrot BB. A fractal's lacunarity, and how it can be tuned and measured. In: Nonnenmacher TF, Losa GA, Weibel ER, eds. Fractals in Biology and Medicine. Basel: Birkhäuser Verlag, 1993; 8-21.
26. Dubuc B, Roques-Carmes C, Tricot C, Zucker SW. The variation method: a technique to estimate the fractal dimension of surfaces. SPIE Visual Commun Image Processing II 1987; 845: 241-248.
27. Dubuc B, Zucker SW, Tricot C, Quiniou JF, Wehbi D. Evaluating the fractal dimension of surfaces. Proc R Soc London Ser A 1989; 425: 113-127.
28. Anguiano E, Pancorbo M, Aguilar M. Fractal characterization by frequency analysis. I. Surfaces. J Microsc 1993; 172: 223-232.
29. Voss RF. Random fractal forgeries. In: Earnshaw RA, ed. Fundamental Algorithms for Computer Graphics. Berlin: Springer-Verlag, 1985; 805-835.
30. Wu H-S, Gil J, Einstein AJ. Fractal characterization of irregularly shaped images by frequency analysis. Fractals 1997; 5: 665-672.
31. Sierpinski W. Sur une courbe cantorienne qui contient une image biunivoque et continue de toute courbe donneé. C R Acad Sci 1916; 162: 629-632.
32. Gefen Y, Aharony A, Mandelbrot BB. Phase transitions on fractals: III. Infinitely ramified lattices. J Phys A: Math Gen 1984; 17: 1277-1289.
33. Lin B, Yang ZR. A suggested lacunarity expression for Sierpinski carpets. J Phys A: Math Gen 1986; 19: L49-L52.
34. Mandelbrot B. Corrélations et texture dans un nouveau modèle d'Univers hiérarchisé, basé sur les ensembles trémas. C R Acad Sci Ser A 1979; 288: 81-83.
35. Allain C, Cloitre M. Characterizing the lacunarity of random and deterministic fractal sets. Phys Rev A 19991; 44: 3552-3558.
36. Plotnick RE, Gardner RH, Hargrove WW, Prestegaard K, Perlmutter M. Lacunarity analysis: a general technique for the analysis of spatial patterns. Phys Rev E 1996; 53: 5461-5468.
37. Einstein AJ, Wu H-S, Gil J. Self-affinity and lacunarity of chromatin texture in benign and malignant breast epithelial cell nuclei. Phys Rev Lett 1998; 80: 397-400.
38. Einstein AJ, Gil J. Classification procedures for diagnosis based on multiple morphometric parameters. Acta Stereol 1996; 15: 15-24.
39. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York: Chapman & Hall, 1993.
40. Kattan MW, Beck JR. Artificial neural networks for medical classification decisions. Arch Pathol Lab Med 1995; 119: 672-677.
41. Hosmer DW, Lemeshow S. Applied Logistic Regression. New York: John Wiley, 1989.
42. Garcia-Romero H, Garcia-Barrios C, Ramos-Gutierrez F. Effects of uncertain results on sensitivity and specificity of diagnostic tests. Lancet 1996; 348: 1745-1746.
43. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986; 323: 533-536.
44. Dytch HE, Wied GL. Artificial neural networks and their use in quantitative pathology. Anal Quant Cytol Histol 1990; 12: 379-393.
45. Deligdisch L, Einstein AJ, Guera D, Gil J. Ovarian dysplasia in epithelial inclusion cysts. A morphometric approach using neural networks. Cancer 1995; 76: 1027-1034.
46. Cross SS, Harrison RF, Kennedy RL. Introduction to neural networks. Lancet 1995; 346: 1075-1079.
47. Levenberg K. A method for the solution of certain non-linear problems in least squares. Q Appl Math 1944; 2: 164-168.
48. Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Indust Appl Math 1963; 11: 431-441.
49. Hagan MT, Manhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Net 1994; 5: 989-993.
50. Oertel YC. Fine Needle Aspiration of the Breast. Boston: Butterworth. 1987.
51. Kline TS, Kline IK. Guides to Clinical Aspiration Biopsy: Breast. New York: Igaku-Shoin, 1989.
52. Frost JK. The Cell in Health and Disease: An Evaluation of Cellular Morphologic Expression of Biologic Behavior. Basel: Karger, 1969.
53. Truong H, Morimoto R, Walts AE, Erler B, Marchevsky A. Neural networks as an aid in the diagnosis of lymphocyte-rich effusions. Anal Quant Cytol Histol 1995; 17: 48-54.
54. Orlando V, Paro R. Chromatin multiprotein complexes involved in the maintenance of transcription patterns. Curr Opin Genet Dev 1995; 5: 174-179.
55. Pentland AP. Fractal-based description of natural scenes. IEEE Trans Pattern Anal Machine Intell 1984; PAMI-6: 661-674.
56. Cox BL, Wang JSY. Fractal surfaces: measurement and applications in the earth sciences. Fractals 1993; 1: 87-115.
57. Astion ML, Wilding P. The application of backpropagation neural networks to problems in pathology and laboratory medicine. Arch Pathol Lab Med 1992; 116: 995-1001.
58. Amari S, Murata N, Müller K-R, Finke M, Yang H. Statistical theory of overtraining - is cross-validation asymptotically effective? In: Touretzky DS, Mozer MC, Masselmo ME, eds. Advances in Neural Information Processing Systems. Vol. 8. Cambridge, MA: MIT Press, 1996; 176-182.
59. Chauvin Y. Generalization dynamics in LMS trained linear networks. In: Lippmann RE, Moody JE, Touretzky DS, eds. Advances in Neural Information Processing Systems. Vol. 3. San Mateo, CA: Morgan Kauffman, 1991; 890-896.
60. King EB, Chew KL, Duarte L, et al. Image cytometric classification of premalignant breast disease in fine needle aspirates. Cancer 1988; 62: 114-124.
61. Young IT, Verbeek PW, Mayall BH. Characterization of chromatin distribution in cell nuclei. Cytometry 1986; 7: 467-474.
62. Hutchinson ML, Isenstein LM, Zahniser DJ. High-resolution and contextual analysis for the diagnosis of fine needle aspirates of breast. Anal Quant Cytol Histol 1991; 13: 351-355.
63. Wolberg WH, Street WN, Mangasarian OL. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Anal Quant Cytol Histol 1995; 17: 77-87.
64. Wolberg WH, Street WN, Mangasarian OL. Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates. Cancer Lett 1994; 77: 163-171.
65. Wilbur DC, Bonfiglio TA, Rutkowski MA, et al. Sensitivity of the AutoPap 300 QC System for cervical cytologic abnormalities. Biopsy data confirmation. Acta Cytol 1996; 40: 127-132.
66. Koss LG, Lin E, Schreiber K, Elgert P, Mango L. Evaluation of the PAPNET cytologic screening system for quality control of cervical smears. Am J Clin Pathol 1994; 101: 220-229.
67. Layfield LJ, Glasgow BJ, Cramer H. Fine-needle aspiration in the management of breast masses. Pathol Annu 1989; 24: 23-62.
68. Ransohoff DF, Feinstein AR. Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. N Engl J Med 1978; 299: 926-930.
69. Begg CB. Biases in the assessment of diagnostic tests. Stat Med 1987; 6: 411-423.
70. Einstein AJ, Bodian CA, Gil J. The relationships among performance measures in the selection of diagnostic tests. Arch Pathol Lab Med 1997; 121: 110-117.
71. Unger PD, Watson CW, Liu Z, Gil J. Morphometric analysis of neoplastic renal aspirates and benign renal tissue. Anal Quant Cytol Histol 1993; 15: 61-66.
72. Rosai J. Borderline epithelial lesions of the breast. Am J Surg Pathol 1991; 15: 209-221.