3D IMAGE RECONSTRUCTION FROM 2D CT SLICES

In this paper, a 3D reconstruction algorithm using CT slices of human pelvis is presented. We propose the method for 3D image reconstruction that is based on a combination of the SURF (Speeded-Up Robust Features) descriptor and SSD (Sum of Squared Differences) matching algorithm using image segmentation with aim to obtain accurate 3D model of human pelvis. Firstly, we apply image filtering for noise removing and smoothing. Next, the filtered image is split into segments using Mean-Shift segmentation algorithm. Secondly, the edges using Canny edge detector are extracted. Then, for each segment we look at the associated corresponding points. The best corresponding points of all the segments using SURF-SSD method were obtained. The smaller is the value of SSD at a particular pixel, the more similarity exists between the first image and the second image in the neighborhood of that pixel. Finally, we have integrated the Mean-Shift segmentation algorithm with the SURF-SSD method. The obtained experimental results demonstrate that the SURF-SSD algorithm in combination with image segmentation provides accurate 3D model of human pelvis.