Dr. Lurdes Orvalho Profile

Dr. Lurdes Orvalho

at Hospital da Luz

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Proceedings Article | 22 May 2020 Paper

Calculation of transfer functions for volume rendering of breast tomosynthesis imaging

Ana M. Mota, Matthew Clarkson, Lurdes Orvalho, Pedro Almeida, Nuno Matela

Proceedings Volume 11513, 1151327 (2020) https://doi.org/10.1117/12.2559932

KEYWORDS: Visualization, Digital breast tomosynthesis, Opacity, Breast, Volume rendering, 3D visualizations, Mammography, Tissues, Breast cancer, Statistical analysis

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Slice by slice visualization of Digital Breast Tomosynthesis (DBT) data is time consuming and can hamper the interpretation of lesions such as clusters of microcalcifications. With a visualization of the object through multiple angles, 3D volume rendering (VR) provides an intuitive understanding of the underlying data at once. 3D VR may play an important complementary role in breast cancer diagnosis. Transfer functions (TFs) are a critical parameter in VR and finding good TFs is a major challenge. The purpose of this work is to study a methodology to automatically generate TFs that result in appropriate and useful VR visualizations of DBT data. For intensity-based TFs, intensity histograms were used to study possible relationships between statistics and critical intensity values in DBT data. The mean of each histogram has proved to be a valid option to automatically calculate those critical values that define these functions. At this stage, eight visualizations were obtained by combining several opacity/color intensity-based functions. Considering the gradient, ten visualizations were obtained. Nine of the ten TFs were constructed considering the peaks of gradient magnitude histograms. The tenth function was a simple linear ramp. Finally, three intensity-based and three gradient-based functions were selected and simultaneously used. This resulted in nine final VR visualizations taking both information into account. The studied approach allowed an automatic generation of opacity/color TFs based on scalar intensity and gradient magnitude histograms. In this way, the preliminary results obtained with this methodology are very encouraging about creating an adequate visualization of DBT data by VR.

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