Automatic needle segmentation in 3D ultrasound images using 3D improved Hough transform

Hua Zhou; Wu Qiu; Mingyue Ding; Songgen Zhang

doi:10.1117/12.770077

17 March 2008 Automatic needle segmentation in 3D ultrasound images using 3D improved Hough transform

Hua Zhou, Wu Qiu, Mingyue Ding, Songgen Zhang

Proceedings Volume 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling; 691821 (2008) https://doi.org/10.1117/12.770077
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

3D ultrasound (US) is a new technology that can be used for a variety of diagnostic applications, such as obstetrical, vascular, and urological imaging, and has been explored greatly potential in the applications of image-guided surgery and therapy. Uterine adenoma and uterine bleeding are the two most prevalent diseases in Chinese woman, and a minimally invasive ablation system using a needle-like RF button electrode is widely used to destroy tumor cells or stop bleeding. To avoid accidents or death of the patient by inaccurate localizations of the electrode and the tumor position during treatment, 3D US guidance system was developed. In this paper, a new automated technique, the 3D Improved Hough Transform (3DIHT) algorithm, which is potentially fast, accurate, and robust to provide needle segmentation in 3D US image for use of 3D US imaging guidance, was presented. Based on the coarse-fine search strategy and a four parameter representation of lines in 3D space, 3DIHT algorithm can segment needles quickly, accurately and robustly. The technique was evaluated using the 3D US images acquired by scanning a water phantom. The segmentation position deviation of the line was less than 2mm and angular deviation was much less than 2°. The average computational time measured on a Pentium IV 2.80GHz PC computer with a 381×381×250 image was less than 2s.

Citation Download Citation

Hua Zhou, Wu Qiu, Mingyue Ding, and Songgen Zhang "Automatic needle segmentation in 3D ultrasound images using 3D improved Hough transform", Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 691821 (17 March 2008); https://doi.org/10.1117/12.770077

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