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Proceedings Paper

New real time needle segmentation technique using grayscale Hough transformation
Author(s): Wu Qiu; Hua Zhou; Mingyue Ding; Songgeng Zhang
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Paper Abstract

Real-time needle segmentation and tracking is very important in image-guided surgery, biopsy, and therapy. In this paper, we described an automated technique to provide real-time needle segmentation from a sequence of 2-D ultrasound images for the use of guidance of a needle to the target in soft tissues. The Hough transform is used to find straight lines or analytic curves in binary image. Hough transform is applied usually to binary images. Hence one needs to convert, initially, the gray level image to a binary one (through thresholding, edge detection, or thinning) in order to apply the HT. While in the process of binarization, some information about line segments in the image may be lost when an inappropriate threshold is used. Gray-Scale Hough Transform can detect the line without binarization. Unfortunately, its high computational cost often prevents it from being applied in real-time applications without the help of specially designed hardware. In this paper, we proposed a needle segmentation technique based on a real-time gray-scale Hough transform. It is composed of an improved Gray Hough Transformation and a coarse-fine search strategy. Furthermore, the RTGHT (Real-Time Gray-Scale Hough Transform) technique is evaluated by patient breast biopsy images. Experiments with patient breast biopsy ultrasound (US) image sequences showed that our approach can segment the biopsy needle in real time (i.e., less than 60 ms) with the angular rms error of about 1° and the position rms error of about 0.5 mm an affordable PC computer without the help of specially designed hardware.

Paper Details

Date Published: 14 November 2007
PDF: 8 pages
Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67890Q (14 November 2007); doi: 10.1117/12.749277
Show Author Affiliations
Wu Qiu, Huazhong Univ. of Science and Technology (China)
Hua Zhou, Huazhong Univ. of Science and Technology (China)
Mingyue Ding, Huazhong Univ. of Science and Technology (China)
Songgeng Zhang, Beijing Tsinghua R&D Industry Institute (China)


Published in SPIE Proceedings Vol. 6789:
MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques

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