Prof. Bo Mo Profile

Prof. Bo Mo

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Proceedings Article | 30 April 2024 Paper

Structure feature extraction algorithm for multi-modal forearm registration

Sibo You, Yan Ding, Bo Mo, Weizhong Zhang, Jiaxin Li, Lingxi Guo, Zhe Yang

Proceedings Volume 13157, 131570H (2024) https://doi.org/10.1117/12.3016103

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While convolutional neural networks have shown promise in medical image registration, their inherent complexity limits their registration speed, particularly for surgical applications. Additionally, traditional feature-based matching methods struggle with multi-modal forearm image registration due to the simplicity of forearm skin textures. To address these issues, we propose a robust forearm feature point extraction method based on the forearm’s structural invariance. We combine this method with thin plate spline interpolation to achieve multi-modal forearm registration. Our approach introduces the Forearm Feature Representation Curve (FFRC) and the Multi-Modal Image Registration Framework (FAM) for aligning forearm images with digital anatomical models. FFRC identifies feature points based on forearm structural characteristics, and FAM employs FFRC for matching point pre-screening before applying an affine transformation. For deformable registration which adds Thin Plate Spline (FAM-TPS) uses the matched points as control points. In our experiments, both FAM and FAM-TPS demonstrate high registration accuracy, with FAM-TPS outperforming conventional feature-based methods. Our framework excels at registering forearm images with varying rotation angles, and we have observed a strong correlation between the feature curve’s peak value and the rotation angle. These results affirm the effectiveness of our approach in achieving precise and resilient registration.

SPIE Journal Paper | 11 March 2020

Weak signal detection algorithm for colored noise in laser guidance

Mengya Li, Bo Mo

OE, Vol. 59, Issue 03, 033103, (March 2020) https://doi.org/10.1117/12.10.1117/1.OE.59.3.033103

KEYWORDS: Signal detection, Interference (communication), Signal to noise ratio, Detection and tracking algorithms, Autoregressive models, Molybdenum, Pulsed laser operation, Computer simulations, Optical engineering, Sensors

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Correlation detection can be used to extract weak signals such as those weakened by noise and attenuation. However, complex background noise contains both white and colored noise. The white noise is independent in the time domain, whereas the colored noise has a certain dependency, and the correlation detection algorithm cannot extract useful signals from the background mixed with colored noise. An improved correlation detection algorithm for whitening colored noise is discussed. Simulation experiments show that for laser signal amplitude similar to the noise amplitude, the correlation detection algorithm can extract the pulse signal, and the white noise background has a significantly improved signal-to-noise ratio of 2.3 times higher than the colored noise background. The experiment also proves that the whitening algorithm discussed can extract weak laser signals very well.

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