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22 May 2020 Improving mammogram visualization by dimming text annotations
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Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115131S (2020)
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium
In digital mammogram visualization, text is usually among the brightest objects. When bright text is near an area of interest in the tissue image, it can annoy the readers and may reduce the perception of tissue details. To mitigate this effect, we propose segmentation of the frame buffer to annotation text and mammographic image using artificial intelligence. An appropriate luminance can be re-assigned to each area (i.e., dim annotations and/or brighten mammographic image to the maximum luminance). Existing text detection and/or segmentation tools we tested did not work for this purpose because they produce false positives (i.e., parts of breast are detected as text and dimmed). That is perhaps because such methods were designed (or trained to, in case of deep learning methods) for natural images. We investigated two state-of-the-art segmentation architectures DeepLabV3+ and Mask R-CNN, as well as a “shallow” text detection method based on maximally stable external regions (MSER). We generate the training data by adding random text to the background of publicly available mammographic images. DeepLabV3+ trained to our data produced promising results while Mask R-CNN and MSER did not. Keywords: Convolutional neural
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Ali R. N. Avanaki, Kathryn S. Espig, Albert Xthona, and Tom R. L. Kimpe "Improving mammogram visualization by dimming text annotations", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115131S (22 May 2020);

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