Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks

Medical Engineering and Physics

Volumn 26, Issue 4, 2004, Pages 303-312

  Bocchi, L ^a   Coppini, G ^b   Nori, J ^c   Valli, G ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b INSTITUTE OF CLINICAL PHYSIOLOGY   (Italy)

^c ASL 10   (Italy)

Author keywords

Artificial neural networks; Fractal dust; Fractional Brownian motion; Mammography; Microcalcification clusters; Microcalcifications

Indexed keywords

ALGORITHMS; CALCIFICATION (BIOCHEMISTRY); DIAGNOSIS; FRACTALS; MAMMOGRAPHY; MATHEMATICAL MODELS;

MALIGNANT LESIONS; MAMMOGRAMS; MICROCALCIFICATIONS;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CALCIFICATION; CANCER DIAGNOSIS; DIAGNOSTIC IMAGING; FRACTAL ANALYSIS; IMAGE ANALYSIS; IMAGE PROCESSING; MAMMOGRAPHY; PRIORITY JOURNAL;

ALGORITHMS; BREAST NEOPLASMS; CALCINOSIS; CLUSTER ANALYSIS; COMPUTER SIMULATION; EXPERT SYSTEMS; FRACTALS; HUMANS; MAMMOGRAPHY; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PRECANCEROUS CONDITIONS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 2342646934     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2003.11.009     Document Type: Article

References (30)

1. ACS. Cancer facts and figures. 1999;American Cancer Society
2. Netsch T., Peitgen H. Scale-space signatures for the detection of clustered microcalcifications in digital mammograms. IEEE Trans Med Im. 18:(9):1999;774-786
3. Gulsrud T.O., Husøy J.H. Optimal filter-based detection of microcalcifications. IEEE Trans Biomed Eng. 48:(11):2001;1272-1280
4. Strickland R.N., Hahn H.I. Wavelet transforms for detecting microcalcifications in mammograms. IEEE Trans Med Im. 15:(2):1996;218-229
5. Thurfjell E., Lernevall K., Taube A. Benefit of independent double reading in a population-based mammography screening program. Radiology. 191:1994;241-244
6. Rangayyan R., Shen L., Desautels J., Bryant H., Terry T., Hreczko N., et al. Improvement of sensitivity of breast cancer diagnosis with adaptive neighborhood contrast enhancement of mammograms. IEEE Trans Inf Techn Biomed. 1:(3):1997;161-170
7. Saha P., Udupa J., Conant E., Chakraborty D., Sullivan D. Breast tissue density quantification via digitized mammograms. IEEE Trans Med Im. 20:(8):2001;792-803
8. Samani A., Bishop J., Plewes D. A constrained modulus reconstruction technique for breast cancer assessment. IEEE Trans Med Im. 20:(9):2001;877-885
9. Zheng L., Chan A. An artificial intelligent system for tumor detection in screening mammogram. IEEE Trans Med Im. 20:(7):2001;559-567
10. Karssemeijer N., te Brake G.M. Detection of stellate distortion in mammograms. IEEE Trans Med Im. 15:(5):1996;611-619
11. Li H., Liu K.J.R., Lo S. Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans Med Im. 16:(6):1997;785-798
12. Cheng H., Luy Y.M., Freimanis R.I. A novel approach to microcalcification detection using fuzzy logic technique. IEEE Trans Med Im. 17:(3):1998;442-450
13. Zwiggelaar R., Briggs C.R.M. Classification of linear structures in mammographic images. Proceedings of MIUA 2001 :2001
14. Li H., Wang Y., Liu K.R., Lo S., Freedman M. Computerized radiographic mass detection - part I: lesion site selection by morphological enhancement and contextual segmentation. IEEE Trans Med Im. 20:(4):2001;289-301
15. Lai S., Li X., Bischof W.F. On techniques for detecting circumscribed masses in mammograms. IEEE Trans Med Im. 8:(4):1989;377-386
16. Sahiner B., Chan H., Petrick N., Wei D., Helvie M.A., Adler D.D., et al. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Im. 15:(5):1996;598-610
17. Petrick N., Chan H.-P., Sahiner B., Wei D. An adaptive density-weighted contrast enhancement filter for mamographic breast mass detection. IEEE Trans Med Im. 15:(1):1996;59-67
18. Zhang W., Doi K., Giger M., Nishikawa R., Schmidt R. An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. Med Phys. 23:(4):1996;595-601
19. Deering W., West B. Fractal physiology. IEEE Eng Med Biol. 1992;40-46
20. Marr D. Vision. 1982;Freeman, New York
21. Dengler J., Behrens S., Desaga J.F. Segmentation of microcalcifications in mammograms. IEEE Trans Med Im. 12:(4):1993;634-642
22. Jain A.K. Fundamentals of image processing. 1989;Prentice Hall
23. Chan H., Doi K., Vyborny C.J., Lam K., Schmidt R.A. Computer-aided detection of microcalcifications in mammograms: methodology and preliminary clinical study. Investigative Radiol. 23:(9):1988;664-671
24. Madelbrot B.B. The fractal geometry of nature. 1983;W.H. Freeman and Co
25. Schepars H, van Beek J, Bassingthwaighte J. Four methods to estimate the fractal dimension from self-affine signals. IEEE Eng Med Biol 1992;11:57-64.
26. Fourneir A., Fussel D., Carpenter L. Computer rendering of stochastic models. Comm ACM. 25:(6):1982;371-384
27. Flandrin P. On the spectrum of fractal Brownian motions. IEEE Trans Inf Th. 35:(1):1989;197-199
28. Bocchi L., Coppini G., Dominicis R.D., Valli G. Tissue characterization from X-ray images. Med Eng Phys. 19:(4):1997;336-342
29. Bishop C. Neural networks for pattern recognition. 1995;University Press, Oxford
30. Nguyen H., Hung W., Thornton B., Thornton E., Lee W. Classification of microcalcifications in mammograms using artificial neural networks. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medical and Biological Society. 2:1998;1006-1008