9 March 2010 Filter learning and evaluation of the computer aided visualization and analysis (CAVA) paradigm for pulmonary nodules using the LIDC-IDRI database
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Abstract
We present a simple rendering scheme for thoracic CT datasets which yields a color coding based on local differential geometry features rather than Hounsfield densities. The local curvatures are computed on several resolution scales and mapped onto different colors, thereby enhancing nodular and tubular structures. The rendering can be used as a navigation device to quickly access points of possible chest anomalies, in particular lung nodules and lymph nodes. The underlying principle is to use the nodule enhancing overview as a possible alternative to classical CAD approaches by avoiding explicit graphical markers. For performance evaluation we have used the LIDC-IDRI lung nodule data base. Our results indicate that the nodule-enhancing overview correlates well with the projection images produced from the IDRI expert annotations, and that we can use this measure to optimize the combination of differential geometry filters.
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Rafael Wiemker, Rafael Wiemker, Ekta Dharaiya, Ekta Dharaiya, Amnon Steinberg, Amnon Steinberg, Thomas Buelow, Thomas Buelow, Axel Saalbach, Axel Saalbach, Torbjörn Vik, Torbjörn Vik, } "Filter learning and evaluation of the computer aided visualization and analysis (CAVA) paradigm for pulmonary nodules using the LIDC-IDRI database", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242U (9 March 2010); doi: 10.1117/12.843797; https://doi.org/10.1117/12.843797
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