14 November 2007 Automatic needle segmentation in 3D ultrasound images using 3D Hough transform
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Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 67890R (2007) https://doi.org/10.1117/12.749339
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
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 an RF button electrode which is needle-like is being used to destroy tumor cells or stop bleeding currently. Now a 3D US guidance system has been developed to avoid accidents or death of the patient by inaccurate localizations of the electrode and the tumor position during treatment. In this paper, we described two automated techniques, the 3D Hough Transform (3DHT) and the 3D Randomized Hough Transform (3DRHT), which is potentially fast, accurate, and robust to provide needle segmentation in 3D US image for use of 3D US imaging guidance. Based on the representation (Φ , θ , ρ , α ) of straight lines in 3D space, we used the 3DHT algorithm to segment needles successfully assumed that the approximate needle position and orientation are known in priori. The 3DRHT algorithm was developed to detect needles quickly without any information of the 3D US images. The needle segmentation techniques were evaluated using the 3D US images acquired by scanning water phantoms. The experiments demonstrated the feasibility of two 3D needle segmentation algorithms described in this paper.
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Hua Zhou, Hua Zhou, Wu Qiu, Wu Qiu, Mingyue Ding, Mingyue Ding, Songgeng Zhang, Songgeng Zhang, } "Automatic needle segmentation in 3D ultrasound images using 3D Hough transform", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67890R (14 November 2007); doi: 10.1117/12.749339; https://doi.org/10.1117/12.749339
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