Accurate motion parameter estimation for colonoscopy tracking using a regression method

Jianfei Liu; Kalpathi R. Subramanian; Terry S. Yoo

doi:10.1117/12.844433

9 March 2010 Accurate motion parameter estimation for colonoscopy tracking using a regression method

Jianfei Liu, Kalpathi R. Subramanian, Terry S. Yoo

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 76241Y (2010) https://doi.org/10.1117/12.844433
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Co-located optical and virtual colonoscopy images have the potential to provide important clinical information during routine colonoscopy procedures. In our earlier work, we presented an optical flow based algorithm to compute egomotion from live colonoscopy video, permitting navigation and visualization of the corresponding patient anatomy. In the original algorithm, motion parameters were estimated using the traditional Least Sum of squares(LS) procedure which can be unstable in the context of optical flow vectors with large errors. In the improved algorithm, we use the Least Median of Squares (LMS) method, a robust regression method for motion parameter estimation. Using the LMS method, we iteratively analyze and converge toward the main distribution of the flow vectors, while disregarding outliers. We show through three experiments the improvement in tracking results obtained using the LMS method, in comparison to the LS estimator. The first experiment demonstrates better spatial accuracy in positioning the virtual camera in the sigmoid colon. The second and third experiments demonstrate the robustness of this estimator, resulting in longer tracked sequences: from 300 to 1310 in the ascending colon, and 410 to 1316 in the transverse colon.

Citation Download Citation

Jianfei Liu, Kalpathi R. Subramanian, and Terry S. Yoo "Accurate motion parameter estimation for colonoscopy tracking using a regression method", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241Y (9 March 2010); https://doi.org/10.1117/12.844433

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KEYWORDS

Colon

Motion estimation

Optical flow

Virtual colonoscopy

Detection and tracking algorithms

Cameras

Optical tracking

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