|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
| ASCII Text | x | ||
| Carey E. Priebe, David J. Marchette, George W. Rogers, "Segmentation of Random Fields Via Borrowed Strength Density Estimation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 494-499, May, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/34.589209, author = {Carey E. Priebe and David J. Marchette and George W. Rogers}, title = {Segmentation of Random Fields Via Borrowed Strength Density Estimation}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {19}, number = {5}, issn = {0162-8828}, year = {1997}, pages = {494-499}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.589209}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Segmentation of Random Fields Via Borrowed Strength Density Estimation IS - 5 SN - 0162-8828 SP494 EP499 EPD - 494-499 A1 - Carey E. Priebe, A1 - David J. Marchette, A1 - George W. Rogers, PY - 1997 KW - Mixture model KW - profile likelihood KW - image analysis KW - digital mammography. VL - 19 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—In many applications, spatial observations must be segmented into homogeneous regions and the number, positions, and shapes of the regions are unknown a priori. Information about the underlying probability distributions for observations in the various regions can be useful in such a procedure, but these distributions are often unknown. Furthermore, while there may be a large number of observations overall, the anticipated regions of interest may be small with few observations from the individual regions. This paper presents a technique designed to address these difficulties. A simple segmentation procedure can be obtained as a clustering of the disjoint subregions obtained through an initial low-level partitioning procedure. Clustering of these subregions based upon a similarity matrix derived from estimates of their marginal probability density functions yields the resultant segmentation. It is shown that this segmentation is improved through the use of a "borrowed strength" density estimation procedure wherein potential similarities between the density functions for the subregions are exploited. The borrowed strength technique is described and the performance of segmentation based on these estimates is investigated through an example from statistical image analysis.
[1] J.Y. Hsiao and A.A. Sawchuck, "Supervised Texture Image Segmentation Using Feature Smoothing and Probabilistic Relaxation Techniques," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 1,279-1,292, 1989.
[2] P. Miller and S. Astley, "Classification of Breast Tissue by Texture Analysis," Image and Vision Computing, vol. 10, pp. 277-282, 1992.
[3] C.E. Priebe, J.L. Solka, R.A. Lorey, G. Rogers, W. Poston, M. Kallergi, W. Qian, L.P. Clarke, and R.A. Clark, "The Application of Fractal Analysis to Mammographic Tissue Classification," Cancer Letters, vol. 77, pp. 183-189, 1994.
[4] R.M. Haralick and L.G. Shapiro, "Image Segmentation Techniques," Computer Vision, Graphics and Image Processing, vol. 29, pp. 100-132, 1985.
[5] R. Jain, R. Kasturi, and B.G. Schunck, Machine Vision.New York: McGraw-Hill, 1995.
[6] J. Serra, Image Analysis and Mathematical Morphology.London: Academic Press, 1982.
[7] L. Vincent and P. Soille, "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, pp. 583-598, 1991.
[8] G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition.New York: John Wiley, 1992.
[9] N.A.C. Cressie, Statistics for Spatial Data.New York: John Wiley, 1993.
[10] B.W. Silverman, Density Estimation for Statistics and Data Analysis.London: Chapman and Hall, 1986.
[11] R.A. Redner and H.F. Walker, "Mixture Densities, Maximum Likelihood and the EM Algorithm," SIAM Rev., vol. 26, pp. 195-239, 1984.
[12] B.S. Everitt and D.J. Hand, Finite Mixture Distributions.London: Chapman and Hall, 1981.
[13] C.E. Priebe, "Adaptive Mixtures," J. Am. Statistical Assoc., vol. 89, pp. 796-806, 1994.
[14] C.E. Priebe, D.J. Marchette, and G.W. Rogers, "Semiparametric Nonhomogeneity Analysis," J. Statistical Planning and Inference, vol. 59, no. 1, pp. 45-60, 1997.
[15] D.R. Cox and N. Reid, "Parameter Orthogonality and Approximate Conditional Inference (with discussion)," J. Royal Statistical Soc., B, vol. 49, pp. 1-39, 1987.
[16] C.E. Priebe, "Nonhomogeneity Analysis Using Borrowed Strength," J. Am. Statistical Assoc., vol. 91, pp. 1,497-1,503, 1996.
[17] P.J. Huber, Robust Statistics.New York: John Wiley, 1981.
[18] C.E. Priebe and D.J. Marchette, "Characterizing Mammographic Tissue via Borrowed Strength Density Estimation," Digital Mammography 1996: Proc. Third Int'l Workshop Digital Mammography,Chicago, June 1996, K. Doi, M.L. Giger, R.M. Nishikawa, and R.A. Schmidt, eds. Amsterdam: Elsevier, 1996.
[19] C.E. Priebe, D.J. Marchette, and G.W. Rogers, "Segmentation of Random Fields via Borrowed Strength Density Estimation," The Johns Hopkins Univ., Dept. of Mathematical Sciences Technical Report #547, 1996b.