Particle swarm optimization for in vivo 3D ultrasound volume registration

  title={Particle swarm optimization for in vivo 3D ultrasound volume registration},
  author={Umer Zeeshan Ijaz and Richard W. Prager and Andrew H. Gee and Graham M. Treece},
As three-dimensional (3D) ultrasound is becoming more and more popular, there has been increased interest in using a position sensor to track the trajectory of the 3D ultrasound probe during the scan. One application is the improvement of image quality by fusion of multiple scans from different orientations. With a position sensor mounted on the probe, the clinicians face additional difficulties, for example, maintaining a line-of-sight between the sensor and the reference point. Therefore, the… 

Optimization strategies for ultrasound volume registration

A hybrid strategy is proposed that replaces the sensor with a combination of three-DOF image registration and an unobtrusive inertial sensor for measuring orientation and it is concluded that normalized mutual information used in the Nelder–Mead simplex algorithm is potentially suitable for the registration task.


An extensive comparative analysis is performed to illustrate the merits and demerits of various available techniques and the applicability of the techniques in brain disorder diagnosis in MR images is explored.



An approach to multimodal biomedical image registration utilizing particle swarm optimization

A new evolutionary approach, particle swarm optimization, is adapted for single-slice 3D-to-3D biomedical image registration and a new hybrid particle swarm technique is proposed that incorporates initial user guidance.

Correction of Probe Pressure Artifacts in Freehand 3D Ultrasound

We present an algorithm which combines non-rigid image-based registration and conventional position sensing to correct probe-pressure-induced registration errors in freehand three-dimensional (3D)

A Novel Approach to Breast Ultrasound Image Segmentation Based on the Characteristics of Breast Tissue and Particle Swarm Optimization

A novel automatic segmentation algorithm based on the characteristics of breast tissue and the eliminating particle swarm optimization (EPSO) clustering analysis that would find wide applications in automatic lesion classification and computer aided diagnosis (CAD) systems of breast cancer.

Applying the Particle Swarm Optimization and Boltzmann Function for Feature Selection and Classification of Lymph Node in Ultrasound Images

A feature selection method that integrates the particle swarm optimization neural network (PSONN) with Boltzmann probabilistic with a support vector machine (SVM) is adopted for Lymph node classification and Experimental results show that the proposed approach decreases the number of the selected features and achieves a high accuracy in classification.

Particle swarm optimization

A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.

The particle swarm optimization algorithm: convergence analysis and parameter selection

  • I. Trelea
  • Computer Science
    Inf. Process. Lett.
  • 2003

Estimating Mutual Information Via Kolmogorov Distance

  • Zhengmin Zhang
  • Computer Science, Mathematics
    IEEE Transactions on Information Theory
  • 2007
By use of a coupling technique, two inequalities are established which set upper bounds to the mutual information of finite discrete random variables in terms of the Kolmogorov distance (variational