In this study, a practical forensics aided steganalyzer (FA-steganalyzer) for heterogeneous bitmap images is constructed, which can properly handle steganalysis problems of mixed image sources including raw single-sampled images and resampled images at different scaling factors. The proposed FA-steganalyzer consists of a resampling detector followed by two corresponding steganalyzers, one of which is used to deal with raw single-sampled images and the other is used for mixed resampled images at different scaling factors. Extensive experimental results show that the proposed FA-steganalyzer outperforms the existing steganalyzer that is trained on a mixed dataset especially at low embedding rates.
In original mammographic images obtained by X-ray radiography, only a small part of detected information is displayed
to the human observer. A method aimed at minimizing image noise while optimizing contrast of mammographic image
features is presented in this paper, for more accurate detection of microcalcification clusters. The method is based on the
contourlet transform, which is a multiresolution, local and directional image representation. The difference from wavelet
and other multiscale expansion lies in that the contourlet transform is constructed by using non-separable filter banks in
discrete-domain, thus it can effectively capture important features of images. The enhancement procedure consists of two
steps: noise filtering by the Stein's thresholding and denoised contourlet coefficients modification via a nonlinear
mapping function. The experimental results have shown an improved visualization of significant mammographic features
by the proposed method. A comparison with other enhancement algorithms is also discussed by employing a measure
named target to background contrast ratio using variance.
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