A User-friendly Tool for Semi-automated Segmentation and Surface Extraction from Color Volume Data Using Geometric Feature-space Operations

Abstract

Summary. Segmentation and surface extraction from 3D imaging data is an important task in medical applications. When dealing with scalar data such as CT or MRI scans, a simple thresholding in form of isosurface extraction is an often a good choice. Isosurface extraction is a standard tool for visualizing scalar volume data. Its generalization to color data such as cryosections, however, is not straightforward. In particular, the user interaction in form of selection of the isovalue needs to be replaced by the selection of a three-dimensional region in feature space. We present a user-friendly tool for segmentation and surface extraction from color volume data. Our approach consists of several automated steps and an intuitive mechanism for user-guided feature selection. Instead of overburden the user with complicated operations in feature space, we perform an automated clustering of the occurring colors and suggest segmentations to the users. The suggestions are presented in a color table, from which the user can select the desired cluster. Simple and intuitive refinement methods are provided, in case the automated clustering algorithms did not immediately generate the desired solution exactly. Finally, a marching technique is presented to extract the boundary surface of the desired cluster in object space.

Multivariate spatial data visualization: a survey

“…The simplest idea is to provide a rough clustering result of the data, and then iteratively correct the clustering result in an appropriate way. Ivanovska and Linsen [29] clustered multivariate data with certain simple and efficient methods such as k-means and median cut, and provided a 2D slice view to correct the clustering results by splitting and merging operations.…”