Visualization of Medical Volume Data Based on Improved K-Means Clustering and Segmentation Rules

Abstract

Accurate extraction and visualization of the target features are important in medical visualization, allowing the user to get a meaningful view of their targets. The traditional way to generate visualization results is 1D TF-based volume visualization, which uses a 1D -TF to determine the optical properties of each voxel. However, it is difficult to distinguish multiple targets by using the 1D TF-based volume visualization. It is also challenging to distinguish accurate targets when the objects' intensity values are similar in volume. Using the traditional transfer function usually fails to extract important target features and generates less accurate results. In this paper, we proposed extracting and segmentation techniques based on K-means algorithm that allows the users to segment and enhances single or multiple targets by a single point to view the features in 3D view. We have applied it to various univariate or multivariate volume datasets from the medical field to demonstrate its effectiveness. Moreover, we have performed both qualitative and quantitative experiments to compare its results against the results from two state-of-the-art techniques and the ground truths. The experimental results showed that our method is able to generate the closest results to the ground truth.