Abstract
The benefits of medical imaging are enormous. Medical images provide considerable amounts of anatomical information and this facilitates medical practitioners in performing effective disease diagnosis and deciding upon the best course of medical treatment. A transition from traditional monochromatic medical images like CT scans, X-Rays, or MRI images to a colored 3D representation of the anatomical structure could further aid medical professionals in extracting valuable medical information. The proposed framework in our research starts with performing color transfer by finding deep semantic correspondence between two medical images: a colored reference image, and a monochromatic CT scan or an MRI image. We extend this idea of reference based colorization technique to perform colored volume rendering from a stack of grayscale medical images. Furthermore, we also propose to use an effective reference image recommendation system to aid for selection of good reference images. With our approach, we successfully perform colored medical volume visualization and essentially eliminate the painstaking process of user interaction with a transfer function to obtain color parameters for volume rendering.
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Ackerman, M.J.: The visible human project. Proc. IEEE 86(3), 504–511 (1998)
Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized PatchMatch correspondence algorithm. In: European Conference on Computer Vision, September 2010
Berger, M., Li, J., Levine, J.A.: A generative model for volume rendering. IEEE Trans. Visual Comput. Graphics 25(4), 1636–1650 (2019)
Cheng, Z., Yang, Q., Sheng, B.: Deep colorization. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV 2015, pp. 415–423. IEEE Computer Society, USA (2015)
Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 1–10 (2008)
Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30, 1323–1341 (2012)
Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Graph. 30(4) (2011)
He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_1
He, M., Chen, D., Liao, J., Sander, P.V., Yuan, L.: Deep exemplar-based colorization. ACM Trans. Graph. 37, 4 (2018)
He, M., Liao, J., Yuan, L., Sander, P.V.: Neural color transfer between images. arXiv abs/1710.00756 (2017)
Hong, F., Liu, C., Yuan, X.: DNN-VolVis: interactive volume visualization supported by deep neural network. In: 2019 IEEE Pacific Visualization Symposium (PacificVis), pp. 282–291 (2019)
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graph. 35(4), 1–11 (2016)
Irony, R., Cohen-Or, D., Lischinski, D.: Colorization by example. In: Proceedings of the Sixteenth Eurographics Conference on Rendering Techniques, EGSR 2005, Goslar, DEU, pp. 201–210. Eurographics Association (2005)
Kindlmann, G., Durkin, J.W.: Semi-automatic generation of transfer functions for direct volume rendering. In: Proceedings of the 1998 IEEE Symposium on Volume Visualization, VVS 1998, New York, NY, USA, pp. 79–86. Association for Computing Machinery (1998)
Kniss, J., Kindlmann, G., Hansen, C.: Multidimensional transfer functions for interactive volume rendering. IEEE Trans. Visual Comput. Graphics 8(3), 270–285 (2002)
Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 577–593. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_35
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: ACM SIGGRAPH 2004 Papers, SIGGRAPH 2004, New York, NY, USA, pp. 689–694. Association for Computing Machinery (2004)
Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual attribute transfer through deep image analogy. ACM Trans. Graph. 36(4) (2017)
Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23, 5638–5653 (2014)
Qu, Y., Wong, T.T., Heng, P.A.: Manga colorization. ACM Trans. Graph. 25(3), 1214–1220 (2006)
Roettger, S., Bauer, M., Stamminger, M.: Spatialized transfer functions. In: Brodlie, K., Duke, D., Joy, K. (eds.) EUROVIS 2005: Eurographics/IEEE VGTC Symposium on Visualization. The Eurographics Association (2005)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556 (2014)
He, T., Hong, L., Kaufman, A., Pfister, H.: Generation of transfer functions with stochastic search techniques. In: Proceedings of Seventh Annual IEEE Visualization 1996, pp. 227–234 (1996)
Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graph. 21(3), 277–280 (2002)
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_40
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Devkota, S., Pattanaik, S. (2020). Referenced Based Color Transfer for Medical Volume Rendering. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_16
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DOI: https://doi.org/10.1007/978-3-030-64556-4_16
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