Fast semi-automatic segmentation based on reduced basis

Wavelet-based semi-automatic live-wire segmentation

The live-wire approach is a well-known algorithm based on a graph search to locate boundaries for image segmentation. We will extend the original cost function, which is solely based on finding strong edges, so that the approach can take a large variety of boundaries into account. The cost function adapts to the local characteristics of a boundary by analyzing a user-defined sample using a cont...

Fast and Robust Semi-automatic Liver Segmentation with Haptic Interaction

We present a method for semi-automatic segmentation of the liver from CT scans. True 3D interaction with haptic feedback is used to facilitate initialization, i.e., seeding of a fast marching algorithm. Four users initialized 52 datasets and the mean interaction time was 40 seconds. The segmentation accuracy was verified by a radiologist. Volume measurements and segmentation precision show that...

Fast semi-automatic segmentation algorithm for Self-Organizing Maps

Self-Organizing Maps (SOM) are very powerful tools for data mining, in particular for visualizing the distribution of the data in very highdimensional data sets. Moreover, the 2D map produced by SOM can be used for unsupervised partitioning of the original data set into categories, provided that this map is somehow adequately segmented in clusters. This is usually done either manually by visual...

Fast Semi-Automatic Plexiform Neurofibroma Tumor Segmentation in MRI Scans

Semi-Automatic Vertebra Segmentation

Most of the existing research in medical image segmentation has focused on magnetic resonance (MR) and computed tomography (CT). Much less work has been done in the segmentation of X-ray images. Nevertheless, medical specialists often examine X-rays images of spinal columns to determine the presence of abnormalities or dysfunctions and to analyse the vertebral mobility. To help them to establis...