Intelligent Volume Visualization for Medical Datasets

For 3D medical data visualization, typically transfer functions (TFs) are employed to classify the data and to assign visual attributes to each material class. However, it is generally difficult to obtain a good transfer function. This paper presents a novel approach for exploring appropriate transfer functions by utilizing artificial intelligent (AI) techniques: the search for a transfer function is reformulated as a global optimization problem, which is subsequently solved by using the particle swarm optimizers (PSO). The initial population of transfer functions is pre-defined by users or selected randomly. The fitness value of each transfer function is defined by user on associated final rendering images or by user-defined objective functions. This approach bridges transfer functions and rendering images, and offers users an image that focuses on the part they interested in the medical datasets.