Name
Title | ||
|---|---|---|
Observation-driven adaptive differential evolution and its application to accurate and smooth bronchoscope three-dimensional motion tracking. |
Abstract | ||
|---|---|---|
This paper proposes an observation-driven adaptive differential evolution algorithm that fuses bronchoscopic video sequences, electromagnetic sensor measurements, and computed tomography images for accurate and smooth bronchoscope three-dimensional motion tracking. Currently an electromagnetic tracker with a position sensor fixed at the bronchoscope tip is commonly used to estimate bronchoscope movements. The large tracking error from directly using sensor measurements, which may be deteriorated heavily by patient respiratory motion and the magnetic field distortion of the tracker, limits clinical applications. How to effectively use sensor measurements for precise and stable bronchoscope electromagnetic tracking remains challenging. We here exploit an observation-driven adaptive differential evolution framework to address such a challenge and boost the tracking accuracy and smoothness. In our framework, two advantageous points are distinguished from other adaptive differential evolution methods: (1) the current observation including sensor measurements and bronchoscopic video images is used in the mutation equation and the fitness computation, respectively and (2) the mutation factor and the crossover rate are determined adaptively on the basis of the current image observation. The experimental results demonstrate that our framework provides much more accurate and smooth bronchoscope tracking than the state-of-the-art methods. Our approach reduces the tracking error from 3.96 to 2.89mm, improves the tracking smoothness from 4.08 to 1.62mm, and increases the visual quality from 0.707 to 0.741. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.media.2015.01.002 | Medical Image Analysis |
Keywords | Field | DocType |
Adaptive differential evolution,Camera 3-D motion tracking,Evolutionary computation,Bronchoscope tracking and navigation,Surgical instrument tracking and navigation | Computer vision,Evolutionary computation,Tracking system,Differential evolution,Artificial intelligence,Smoothness,Distortion,Position sensor,Mathematics,Match moving,Tracking error | Journal |
Volume | Issue | ISSN |
24 | 1 | 1361-8415 |
Citations | PageRank | References |
2 | 0.38 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiongbiao Luo | 1 | 124 | 22.22 |
| Ying Wan | 2 | 2 | 2.07 |
| Xiangjian He | 3 | 932 | 132.03 |
| Mori, K. | 4 | 14 | 2.81 |