Binocular endoscopic 3-D scene reconstruction using color and gradient-boosted aggregation stereo matching for robotic surgery
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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© Springer International Publishing Switzerland 2015. This paper seeks to develop fast and accurate endoscopic stereo 3-D scene reconstruction for image-guided robotic surgery. Although stereo 3-D reconstruction techniques have been widely discussed over the last few decades, they still remain challenging for endoscopic stereo images with photometric variations, noise, and specularities. To address these limitations, we propose a robust stereo matching framework that constructs cost function on the basis of image gradient and three-channel color information for endoscopic stereo scene 3-D reconstruction. Color information is powerful for textureless stereo pairs and gradient is robust to texture structures under noise and illumination change. We evaluate our stereo matching framework on clinical patient stereoscopic endoscopic sequence data. Experimental results demonstrate that our approach significantly outperforms current available methods. In particular, our framework provided 99.5% reconstructed density of stereo images compared to other available matching strategies which achieved at the most an 87.6% reconstruction of the scene.