A real‐time biopsy needle segmentation technique using Hough Transform

Abstract

Real-time needle segmentation and tracking is very important in image-guided surgery, biopsy, and therapy. Due to its robustness to the addition of extraneous noise, the Hough Transform is one of the most powerful line-detection techniques nowadays and has been widely used in different areas. Unfortunately, its high computation needs often prevent it from being applied in real-time applications without the help of specially designed hardware. In order to solve this problem, a variety of fast implementation algorithms have been proposed. However, none of them can be performed in a real time on an affordable computer. In this paper, we describe a fast implementation of the Hough Transform based on coarse-fine search and the determination of the optimal image resolution. Compared to conventional techniques, our approach decreases the time for needle segmentation by an order of magnitude. Experiments with agar phantom and patient breast biopsy ultrasound (US) image sequences showed that our approach can segment the biopsy needle in real time (i.e., less than 33 ms) on an affordable PC computer without the help of specially designed hardware with the angular rms error of about 1 degrees and the position rms error of about 0.5 mm.

Three-Dimensional Needle Shape Estimation in TRUS-Guided Prostate Brachytherapy Using 2-D Ultrasound Images

“…Research on curved needle segmentation from 3D US includes a generalized Radon transform with Bezier curves [6], and the Hough transform combined with ray casting and polynomial approximation [7]. Alternatives to Radon/Hough transform-based methods include difference imaging techniques [8], [9] or RANSAC (random sample consensus)-based methods [10]- [12].…”

Projection-Based Needle Segmentation in 3D Ultrasound Images

“…In the first projected image, we used a 2D needle segmentation technique, such as the flood-filling-based 2D US image segmentation method [7] or the real-time Hough transform technique described in Ref. [14] to segment the needle. Using the initial projection direction and the needle direction segmented from the first projected image, a plane containing the needle, called the needle plane, is determined.…”

Robotic Needle Steering: Design, Modeling, Planning, and Image Guidance

“…US images tend to be noisy, due to reflections, reverberations, shadows, air pockets, and biological speckle, which makes needle localization challenging. Some needle localization methods use 2D images [15,20,53], while others compound a 3D volume from a tracked sweep of 2D images [2,21]. For completeness, we note that, due to current limitations on voxel resolution and transfer speed, 3D US probes have not been practical for image-based needle guidance.…”

“…To localize straight needles in 2D ultrasound, Ding et al introduced a sophisticated derivative of the Hough transform [20]. Cheung et al proposed an enhancement algorithm that maximizes the received reflections by steering the ultrasound beam to be precisely perpendicular to the needle [15].…”

“…Usually, a cascade of basic image filters (such as thresholding, edge detection, image smoothing and noise removal filters) are combined with more sophisticated feature detections routines, such as a variant of Hough transform. Significant literature exists on the theory, use, and extension of Hough transform; a succinct summary and background reading are given in [20]. Many localization methods entail two steps: first, points or fragments of the needle are extracted from the images and then a 3D geometric model (straight line, polynomial, etc.)…”