In many volume segmentation and visualization tasks, the ability to correctly identify the boundary surface of each volumetric feature of interest in the data is desirable. This surface can be used in subsequent quantitative study of the segmented features. In this paper, we present an automatic approach to generate accurate representations of a feature of interest from volume segmentation. Our method first locates a set of points which tightly define the boundary of the volumetric feature ...

Filter banks are a class of signal processing techniques that can be used to reveal the local energy of a signal at multiple scales. Utilizing such filtering allows us to consider local texture and other data characteristics, and permits volume classification and visualization that cannot be accomplished easily using conventional, transfer function-based methods. Our filter bank approach increases the dimensionality, and thus, the complexity of the classification task ...

We present a new hierarchical graph representation for volume data as well as its associated operations to enable interactive feature segmentation for high-resolution volume data. Our method constructs a low-resolution graph which represents a coarser resolution of the data. This graph enables the user to interactively sample and edit a feature of interest by drawing strokes on data slices. ...

In volume data visualization, the classification step is used to determine voxel visibility and is usually carried out through the interactive editing of a transfer function that defines a mapping between voxel value and color/opacity. This approach is limited by the difficulties in working effectively in the transfer function space beyond two dimensions. We present a new approach to the volume classification problem which couples machine learning and a painting metaphor to allow more sophisticated classification in an intuitive manner ...

In volume visualization, users typically specify transfer functions to classify the data and assign visual attributes to each material class. Higher-dimensional classication makes it easier to differentiate material classes since more data properties are considered. One of the difculties in using higher-dimensional classication is the absence of appropriate user interfaces. We introduce an intuitive user interface that allows the user to work in the cluster space, which shows the material classes with a set of material widgets, rather than work in the transfer function space ...

In the traditional volume visualization paradigm, the user specifies a transfer function that assigns each scalar value to a color and opacity by defining an opacity and a color map function. The transfer function has two limitations. First, the user must define curves based on histogram and value rather than seeing and working with the volume itself. Second, the transfer function is inflexible in classifying regions of interest, where values at a voxel such as intensity and gradient are used to differentiate material, not taking into account additional properties such as texture and position ...

Interactive data visualization is inherently an iterative trial-and-error process searching for an ideal set of parameters for classifying and rendering features of interest in the data. This paper presents 3-d region growing based techniques that can assist the users to locate and define features of interest in volume data more quickly and more accurately. One technique employs partial region growing to generate a 2-d transfer function that effectively reveals the full features of interest ...

This paper describes a set of techniques developed for the visualization of high-resolution volume data generated from industrial computed tomography for nondestructive testing (NDT) applications. Because the data are typically noisy and contain ne features, direct volume rendering methods do not always give us satisfactory results. We have coupled region growing techniques and a 2D histogram interface to facilitate volumetric feature extraction. ...

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 ...

...

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 ...