Filtered 2D Contour-Based Reconstruction of 3D STL Model from CT-DICOM Images

TL;DR

This paper presents a method for reconstructing accurate 3D STL models from 2D CT-DICOM images by filtering contour data to reduce segmentation errors and improve geometric fidelity.

Contribution

It introduces a filtering approach for 2D contour points before 3D reconstruction, enhancing model accuracy from low-resolution segmented images.

Findings

Filtered contour points improve 3D model geometry.

Method verified on basic shapes and pelvic bone ROI.

Filtering reduces deviations in reconstructed models.

Abstract

Reconstructing a 3D Stereo-lithography (STL) Model from 2D Contours of scanned structure in Digital Imaging and Communication in Medicine (DICOM) images is crucial to understand the geometry and deformity. Computed Tomography (CT) images are processed to enhance the contrast, reduce the noise followed by smoothing. The processed CT images are segmented using thresholding technique. 2D contour data points are extracted from segmented CT images and are used to construct 3D STL Models. The 2D contour data points may contain outliers as a result of segmentation of low resolution images and the geometry of the constructed 3D structure deviate from the actual. To cope with the imperfections in segmentation process, in this work we propose to use filtered 2D contour data points to reconstruct 3D STL Model. The filtered 2D contour points of each image are delaunay triangulated and joined layer-by-layer to reconstruct the 3D STL model. The 3D STL Model reconstruction is verified on i) 2D Data points of basic shapes and ii) Region of Interest (ROI) of human pelvic bone and are presented as case studies. The 3D STL model constructed from 2D contour data points of ROI of segmented pelvic bone with and without filtering are presented. The 3D STL model reconstructed from filtered 2D data points improved the geometry of model compared to the model reconstructed without filtering 2D data points.