A stochastic model for automated detection of calcifications in digital mammograms
A stochastic model is developed to enable pattern classification in mammograms based on Bayesian decision theory. Labeling of the image is performed by a deterministic relaxation scheme in which both image data and prior beliefs are weighted simultaneously. The image data is represented by two parameter images representing local contrast and shape. Involving shape is necessary to distinguish thin patches of connective tissue from microcalcifications. A random field models contextual relations between pixel labels. Long range interaction is introduced to express the fact that calcifications do occur in clusters. This ensures that faint spots are only interpreted as calcifications if they are in the neighborhood of others.
Keywordspattern recognition image analysis segmentation mammography
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- Astley S M and Taylor C J (1990) Combining cues for mammographic abnormalities. Proc. 1st. Brit. Mach. Vision Conf., 253–258.Google Scholar
- Ballard D H (1984) Parameter nets. Artificial Intelligence 22:235–267Google Scholar
- Besag J E (1986) On the statistical analysis of dirty pictures. J. Royal. Statist. Soc., Ser. B 48:259–302Google Scholar
- Chan H P, Doi K, Vyborny C J, Lam K L and Schmidt R A (1988) Computer-Aided detection of micro-calcifications in mammograms. Invest. Radiol. 23 (9): 664–671.Google Scholar
- Davies D H and Dance D R (1990) Automatic computer detection of clustered calcifications in digital mammograms. Phys. Med. Biol. 35 (8): 1111–1118.Google Scholar
- Dubes R C, Jain A K (1989) Random field models in image analysis. Journal of Applied Statistics 16 (2):131–164Google Scholar
- Fam B H, Olson S L, Winter P F and Scholz F J (1988) Algorithm for the detection of fine clustered calcifications on film mammograms. Radiology 169 (2):333–337Google Scholar
- Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern. Anal. Machine. Intell. 6:721–741Google Scholar
- Holland R, Hendriks J H C L, Verbeek A L M, Mravunac M, and Schuurmans J H (1990) Extent, distribution, and mammographic/histological correlations of breast ductal carcinoma in situ. Lancet, 335:519–522.Google Scholar
- Illingworth J and Kittler J (1988) A survey of the Hough Transform. Comp. Vision, Graph. and Im. Proc. 44:87–116Google Scholar
- Karssemeijer N (1989) A statistical method for automatic labeling of tissues in medical images. Mach. Vision and Appl. 3:75–86Google Scholar
- Karssemeijer N and van Erning L J Th O (1991) Iso-precision scaling of digitized mammograms to facilitate image analysis. SPIE Med. Im. V, Image Processing (in press)Google Scholar