Abstract
Okatani and Deguchi [13] proposed a local Shape from Shading (SFS) method for endoscope images by assuming the point light, which is close to the projection center, to be at the projection center. We extended and modified their method and devised a global SFS algorithm for the reconstruction of the complex shape of an internal organ. Since the surface of an organ is not Lambertian in general, we obtained the bi-directional reflection distribution function (BRDF) curve by calibration using a robot arm to achieve accurate endoscope orientation and positioning. Inspired by the idea of Kimmel and Bruckstein [8], global SFS method is based on the identification of singular points on the distance map, which each has the surface normal pointing towards the light source. Equal distance contours are propagated from each singular point using a level set method to get a local distance map of the surface. This is repeated for all singular points. After that, a set of local distance maps are selected to be merged together to construct a global distance map using a new scheme. The shape of the object can then be obtained from the global distance map. Simulated and real experiments were performed to verify the algorithm. Experimental result of global SFS from a single real endoscope image of a human lung is quite good.
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Yeung, S.Y., Tsui, H.T., Yim, A. (1999). Global Shape from Shading for an Endoscope Image. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_35
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DOI: https://doi.org/10.1007/10704282_35
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