Qian W, Clarke LP, Kallergi M, Li H, Velthuizen R, Clark RA, et al. Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography. SPIE Biomed Image Process Biomed Visual 1993;12(4):634--42.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....literature, several techniques have been proposed to detect the presence of microcalcifications using various methodologies. Concerning image segmentation and specification of regions of interest (ROIs) several methods have been proposed such as classical image filtering and local thresholding [9,12,39,45], techniques based on mathematical morphology [13,60] stochastic fractal models [25,26] wavelet analysis [3,7,22,23,46,52,56,57] and multiscale analysis based on a specialized Gaussian and Peitgen [32] Furthermore, various classification methodologies have been reported for the characterization ....

Qian W, Clarke LP, Kallergi M, Li H, Velthuizen R, Clark RA, et al. Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography. SPIE Biomed Image Process Biomed Visual 1993;12(4):634--42.

....discoveries show that a multiresolution approach exists in human vision system, thus leading to an idea of using wavelet based multiresolution analysis for mammographic image processing. Wavelet approach has been used by [Strickland and Hahn, 1997] for detection of microcalcifications, while [Qian et al. 1993] used wavelets and tree structured nonlinear filtering for microcalcification segmentation. Laine, Fan, and Yang, 1995] used wavelets for contrast enhancement in digital mammography, as well as many other authors. Microcalcifications usually come in clusters, having very sharp edges, and usually ....

W. Chian, L. P. Clarke, M.Kallergi, H. D. Li, R. P. Velthuizen, R. A. Clarke, and M. L. Silbigier: "Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography," Biomed. Image Processing and Biomed. Visualization, Proc. SPIE 1905, pp. 509-521, 1993

....the image data and extracting predetermined image features, then localizing regions of interest (ROIs) which can be examined further for potential anomalies. High degrees of sensitivity (85 to 100 ) have been achieved using several of these techniques (see, for example, 12] 13] 14] 15] [ 16], 39] 40] 41] 42] 43] 45] but many have been hampered by high falsepositive rates (1 to 4 false positive identifications per image) and hence low specificity (see, for example, 12] 15] 16] 18] 30] 43] 45] The problem of false positives is compounded by the fact that ....

.... been achieved using several of these techniques (see, for example, 12] 13] 14] 15] 16] 39] 40] 41] 42] 43] 45] but many have been hampered by high falsepositive rates (1 to 4 false positive identifications per image) and hence low specificity (see, for example, 12] 15] [ 16], 18] 30] 43] 45] The problem of false positives is compounded by the fact that false positive rates are reported per image, not per case. Since many radiologic examinations include more than one image, the actual number of false positives per case may be a multiple of those reported. In ....

Clarke L.P., Kallergi M., Qian W., LI H.D., Clark R.A., Silbiger M.L., "Tree-structured non-linear filter and wavelet transform for microcalcification segmentation in digital mammography," Cancer Lett 1994; 77:173-181.

....reduces the resolution of the image, the representation of an image by its low resolution and detail images is often called a multiresolution (multiscale) representation. Wavelets have been successfully used in a number of applications: e.g. the detection of microcalcifications in mammographics (Clarke et al. 1994), evolutionary ecology (Kiltie et al. 1995) the characterisation of corrosion morphology from microscopic images (Livens et al. 1996) speech analysis and the characterisation of voice dysphonia (Van de Wouwer et al. 1996) We propose a method for the automated classification of microscopic ....

Clarke, L., Kallergi, M., Qian, W., Li, H.-D., Clark, R., and Silbiger, M. (1994). Tree-structured non-linear filter and wavelet transform for microcalcification segmentation in digital mammography.

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