Image-Based Gating of Intravascular Ultrasound Sequences Using the Phase Information of Dual-Tree Complex Wavelet Transform Coefficients.

Intravascular ultrasound (IVUS) is a widely used interventional imaging technique for the assessment of atherosclerosis plaque. Due to pulsatile heart motions, transverse and longitudinal motions are observed during in vivo pullbacks of IVUS sequences. These motion artifacts can mislead the volume-based data retrieved from IVUS studies and hinder the visualization of the vessel condition.

An efficient neural network based method for medical image segmentation.

The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together.