Ultrasound Needle Segmentation via Hough Transform and Beam Steering
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Solution Overview
Problem
Current ultrasound image guidance techniques face challenges in accurately and efficiently segmenting needles, especially when they are not deeply inserted, due to interference from specular structures and artifacts, requiring user interaction and intensity-based edge detection which is not robust enough for early detection.
Innovation Solution
A classification-based medical image segmentation apparatus using a pixel-wise needle classifier and statistical boosting of wavelet features, combined with US beam steering, to automatically segment needles without relying on the assumption that the needle is the brightest linear object, allowing for earlier detection and enhanced visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If intensity-based edge detection with threshold is used for needle segmentation, then the detection process is simple, but the reliability is low due to narrow range of effectiveness and interference from specular structures
Solution Approach 1:
The patent transforms the segmentation problem from intensity-based thresholding to parameter-space analysis using Hough transform. By converting edge points into parameter votes (rho, theta) in Hough space, the system changes the representation parameters from pixel intensity to geometric parameters, enabling reliable needle detection even when intensity thresholds are difficult to set due to varying tissue backgrounds and specular reflections.
Solution Approach 2:
The patent introduces an intermediary representation (Hough parameter space) between the raw image data and the final needle detection. Instead of directly thresholding image intensities, the system first detects edges, then maps these edges into Hough parameter space where linear structures accumulate votes. This intermediary transformation space separates the detection process from intensity variations, improving reliability.
2Illumination intensity
If beam steering is applied to enhance needle visibility, then needle visualization is improved, but artifacts such as grating lobes increase
Solution Approach 1:
The patent extracts the needle signal from the noisy background by transforming the problem into parameter space. Using Hough transform, linear structures (needle) accumulate votes in specific (rho, theta) bins, while artifacts and random noise distribute votes more uniformly. This extraction in parameter space separates the desired needle signal from harmful artifacts, allowing clean needle detection even when beam steering introduces grating lobes.
3Productivity
If automatic needle segmentation is performed without user interaction, then productivity is improved, but the difficulty of detecting and measuring increases due to early insertion detection challenges
Solution Approach 1:
The patent implements self-service automation where the system automatically performs needle segmentation without user interaction. The combination of edge detection, Hough transform parameter accumulation, and automatic thresholding in Hough space creates a self-contained pipeline that autonomously identifies needles even during early insertion when only small portions are visible. The system serves itself by automatically adapting to varying insertion depths and tissue conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables reliable and automatic needle segmentation, allowing for earlier detection during insertion, reduced user intervention, and improved visualization by setting optimal beam steering and image processing parameters, enhancing clinical workflow and enabling image fusion with pre-operative modalities.
Implementation Method 1
electronically steer the US beam in a lateral direction perpendicular to the needle orientation, producing strong specular reflections that enhance needle visualization dramatically
Data Source
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AI summary
A classification-based medical image segmentation apparatus includes an ultrasound image acquisition device configured for acquiring, from ultrasound, an image depicting a medical instrument such as needle; and machine-learning-based-classification circuitry configured for using machine-learning-based-classification to, dynamically responsive to the acquiring, segment the instrument by operating on information (212) derived from the image. The segmenting can be accomplished via statistical boosting (220) of parameters of wavelet features. Each pixel (216) of the image is identified as "needle" or "background." The whole process of acquiring an image, segmenting the needle, and displaying an image with a visually enhanced and artifact-free needle-only overlay may be performed automatically and without the need for user intervention.