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| Three-Dimensional Reconstruction Method of Lesion Slices Based on Refined Isosurface |
| Tan Ling1, Liang Ying1, Ma Wenjie1, Xia Jingming2# *, Zhu Jining1 |
1(Engineering Research Center of Digital Forensics Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China);
2(School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China) |
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Abstract Three-dimensional reconstruction of brain tissue lesion slices is of great significance for understanding status of glioblastoma, and can be used for various clinical applications including differential diagnosis and surgical simulation. The marching cubes (MC) algorithm is a classic polygonal surface reconstruction algorithm with the advantages of simplicity and ease of implementation, however, its efficiency is low with obvious cascade phenomena. To solve this problem, this study proposed a lesion slice spatial stacking reconstruction method (SSR-RI) refining the isosurface topology, aiming to achieve optimization of topology configuration and operational efficiency. SSR-RI was applied to process adjacent images of MRI slices by constructing a spatial coordinate system. In order to improve the vulnerability of image components in the bilinear interpolation method, an adaptive spatial interpolation method was proposed, which adaptively selected interpolation points according to the change of gray value to expand the surrounding. On the basis of combining isonormal vertices, a stacked reconstruction method for refining isosurface extraction was designed to improve stacking speed and effectively reduce cascade problems. In order to further optimize the iterative reconstruction effect of SSR-RI, an improved local reflection illumination (PR) was proposed to draw 3D lesions, and the reconstruction volume was optimized by using specular color reflection (SCR) and specular exponent (SE). There were 618 cases of brain tumor segmentation dataset BraTS used to carry out iterative reconstruction experiments to verify the performance of this research method. Experimental results showed that the reconstruction time of the proposed algorithm was only 2.124 seconds, with an F-score value of 0.845 and an SSIM value of 0.81. Compared to the MC algorithm, the reconstruction time was reduced by 38%, and the F-score value and SSIM value were increased by 30.89% and 38.4%, respectively. The structure of the reconstructed volume sequence was compact, and the visual effect was more stereoscopic and textured, which effectively improved the rendering efficiency of iterative reconstruction.
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Received: 11 October 2022
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