Volumetric medical image datasets contain vital information for noninvasive diagnosis, treatment planning and prognosis. However, direct and unlimited query of such datasets is hindered due to the unstructured nature of the imaging data. This study is a step towards the unlimited query of medical image datasets by focusing on specific Structures of Interest (SOI). A requirement in achieving this objective is having both the surface and volume models of the SOI. However, typically, only the surface model is available. Therefore, this study focuses on creating a fast method to convert a surface model to a volume model. Three methods (1D, 2D and 3D) are proposed and evaluated using simulated and real data of Deep Perisylvian Area (DPSA) within the human brain. The 1D method takes 80 msec for DPSA model; about 4 times faster than 2D method and 7.4 fold faster than 3D method, with over 97% accuracy. The proposed 1D method is feasible for surface to volume conversion in computer aided diagnosis, treatment planning and prognosis systems containing large amounts of unstructured medical images.


Medical image analysis, Medical image querying, Medical image database, Medical informatics, Content-based image retrieval