Improving Accuracy for Shape Recovery from Medical Endoscope Image Using Neural Network Learning

Abstract

The VBW (Vogel-Breuß-Weickert) model is proposed as a method to recover 3-D shape under point light source illumination and perspective projection. However, the VBW model recovers relative, not absolute, shape. Here, shape modification is introduced to recover the exact shape. Modification is applied to the output of the VBW model. First, a local brightest point is used to estimate the reflectance parameter from two images obtained with movement of the endoscope camera in depth. After the reflectance parameter is estimated, a sphere image is generated and used for Radial Basis Function Neural Network (RBF-NN) learning. The NN implements the shape modification. NN input is the gradient parameters produced by the VBW model for the generated sphere. NN output is the true gradient parameters for the true values of the generated sphere. Depth can then be recovered using the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment. Although it is also possible modify the shape by using regression analysis instead of neural network, it was confirmed that NN performs better accuracy than regression analysis.