This paper introduces an interactive classification technique for volume data, called ISpace, which uses Independent Component Analysis (ICA) and a multidimensional histogram of the volume data in a transformed space. Essentially, classification in the volume domain becomes equivalent to interactive clipping in the ICA space, which as demonstrated using several examples is more intuitive and direct for the user to classify data. The result is an opacity transfer function defined for rendering multivariate scalar volume data.

Hierarchical, texture-based rendering is a key technology for exploring largescale data sets. We describe a framework for an interactive rendering system based on a client/server model. The system supports various output media from immersive 3-D environments to desktop based rendering systems. It uses web-based transport mechanisms to transfer the data between the server and the client application. This allows us to access and explore large-scale data sets from remote locations over the Internet. Hierarchical space-subdivision,wavelet compression, and progressive data transmission are used to visualize the data on the client side.

We present a semi-automatic technique for segmenting a large cryo-sliced human brain data set that contains 753 highresolution RGB color images. This human brain data set presents a number of unique challenges to segmentation and visualization due to its size (over 7 GB) as well as the fact that each image not only shows the current slice of the brain but also unsliced ''deeper layers'' of the brain. These challenges are not present in traditional MRI and CT data sets. We have found that segmenting this data set can be made easier by using the YIQ color model and morphology. We have used a hardware-assisted interactive volume renderer to evaluate our segmentation results.