Layered manufacturing for medical imaging data

Abstract

Layered manufacturing techniques have been successfully employed to construct scanned objects from three-dimensional medical image data sets. The printed physical models are useful tools for anatomical exploration, surgical planning, teaching, and related medical applications. Before fabricating scanned objects, we have to first build watertight geometrical representations of the target objects from medical image data sets. Many algorithms had been developed to fulfill this duty. However, some of these methods require extra efforts to resolve ambiguity problems and to fix broken surfaces. Other methods cannot generate legitimate models for layered manufacturing. To alleviate these problems, this article presents a modeling procedure to efficiently create geometrical representations of objects from computerized tomography scan and magnetic resonance imaging data sets. The proposed procedure extracts the iso-surface of the target object from the input data set at the first step. Then it converts the iso-surface into a three-dimensional image and filters this three-dimensional image using morphological operators to remove dangling parts and noises. At the next step, a distance field is computed in the three-dimensional image space to approximate the surface of the target object. Then the proposed procedure smooths the distance field to soothe sharp corners and edges of the target object. Finally, a boundary representation is built from the distance field to model the target object. Compared with conventional modeling techniques, the proposed method possesses the following advantages: (1) it reduces human efforts involved in the geometrical modeling process. (2) It can construct both solid and hollow models for the target object, and wall thickness of the hollow models is adjustable. (3) The resultant boundary representation guarantees to form a watertight solid geometry, which is printable using three-dimensional printers. (4) The proposed procedure allows users to tune the precision of the geometrical model to compromise with the available computational resources.