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- W1552923133 abstract "A novel method for accurate detection of regions of interest (ROIs) that contain circumscribed lesions in X-rays mammograms based on bilateral subtraction is presented. Implementing this method requires left and right breast images alignment using a cross-correlation criterion followed by a windowing analysis in mammogram pairs. Furthermore, a set of qualification criteria is employed to filter these regions, retaining the most suspicious for which a Radial-Basis Function Neural Network makes the final decision marking them as ROIs that contain abnormal tissue. Extensive experiments have shown that the proposed method detects the location of the circumscribed lesions with accuracy of 95.8% in the MIAS database.KeywordsTrue Positive RateDigital MammographyWindow AnalysisAbnormal TissueDigitize MammogramThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1552923133 date "2004-01-01" @default.
- W1552923133 modified "2023-09-27" @default.
- W1552923133 title "Automatic Detection of Abnormal Tissue in Bilateral Mammograms Using Neural Networks" @default.
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- W1552923133 doi "https://doi.org/10.1007/978-3-540-24674-9_28" @default.
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