It is difficult to distinguish the inferior alveolar nerve (IAN) from other tissues inside the IAN canal due to their similar CT values in the X image which are smaller than that of the bones. The direct reconstruction, therefore, is difficult to achieve the effects. The traditional clinical treatments mainly rely on doctors' manually drawing the X images so that some subjective results could not be avoided. This paper proposes the partition reconstruction of IAN canal based on shape features. According to the anatomical features of the IAN canal, we divided the image into three parts and treated the three parts differently. For the first, the directly part of the mandibular, we used Shape-driven Level-set Algorithm Restrained by Local Information (BSLARLI) segment IAN canal. For the second part, the mandibular body, we used Space B-spline curve fitting IAN canal's center, then along the center curve established the cross section. And for the third part, the mental foramen, we used an adaptive threshold Canny algorithm to extract IAN canal's edge to find center curve, and then along it established the cross section similarly. Finally we used the Visualization Toolkit (VTK) to reconstruct the CT data as mentioned above. The VTK reconstruction result by setting a different opacity and color values of tissues CT data can perspectively display the INA canal clearly. The reconstruction result by using this method is smoother than that using the segmentation results and the anatomical structure of mental foramen position is similar to the real tissues, so it provides an effective method for locating the spatial position of the IAN canal for implant surgeries.
Integrating visualization toolkit and the capability of interaction, bidirectional communication and graphics rendering which provided by HTML5, we explored and experimented on the feasibility of remote medical image reconstruction and interaction in pure Web. We prompted server-centric method which did not need to download the big medical data to local connections and avoided considering network transmission pressure and the three-dimensional (3D) rendering capability of client hardware. The method integrated remote medical image reconstruction and interaction into Web seamlessly, which was applicable to lower-end computers and mobile devices. Finally, we tested this method in the Internet and achieved real-time effects. This Web-based 3D reconstruction and interaction method, which crosses over internet terminals and performance limited devices, may be useful for remote medical assistant.