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Ultrasonic image low-rank analysis based thyroid lesion image identification method

An ultrasound image and image recognition technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problem of no thyroid lesion image recognition method, and achieve high time efficiency of solving algorithm, reduce data scale, and improve algorithm efficiency. Effect

Active Publication Date: 2016-03-23
BEIHANG UNIV
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Problems solved by technology

[0004] According to the survey, there is currently no method for identifying thyroid lesion images

Method used

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  • Ultrasonic image low-rank analysis based thyroid lesion image identification method
  • Ultrasonic image low-rank analysis based thyroid lesion image identification method
  • Ultrasonic image low-rank analysis based thyroid lesion image identification method

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Embodiment Construction

[0052] figure 1 The overall processing flow of thyroid lesion identification based on low-rank analysis of ultrasound images is given, and the present invention will be further described below in conjunction with other drawings and specific embodiments.

[0053] The present invention provides a method for identifying thyroid lesions based on low-rank analysis of ultrasound images. The main steps are introduced as follows:

[0054] 1. Image block feature extraction and description based on superpixel hierarchical segmentation

[0055] The superpixel segmentation based on linear iterative clustering (SLIC: SimpleLinearIterativeClustering) is based on the similarity of pixel positions and the similarity of pixel colors, and through continuous simple linear clustering, the image is divided into multiple sub-regions to form superpixels. Linear iterative clustering is performed in a five-dimensional space (Labxy). Among them, Lab is the color vector of the pixel in the CIELAB colo...

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Abstract

The invention provides an ultrasonic image low-rank analysis based thyroid lesion image identification method. The method comprises three steps of extracting and describing image block-shaped features based on superpixel hierarchy partition: extracting image features in a multi-scale and hierarchical way by taking a superpixel as a unit, removing redundant information of the image by virtue of the superpixel, lowering the complexity of a subsequent image processing task, and giving consideration to acquisition of global and local information; identifying thyroid based on feature space low-rank reconstruction error analysis: according to the low-rank property of image structure information, calculating the similarity between test data and a dictionary in a manner of optimizing a lowest rank, calculating a reconstruction error, and identifying a thyroid region in combination with a graph-cut segmentation algorithm; and detecting a thyroid lesion based on local low-rank decomposition: dividing a data matrix into a matrix with the low-rank property and an error matrix with the sparsity by adopting a low-rank decomposition method, calculating a sparse error, performing significance detection, and determining a lesion region.

Description

technical field [0001] The invention relates to an image recognition method for thyroid lesions based on low-rank analysis of ultrasonic images. Background technique [0002] Vision is an important source of information for human beings. In the information age when computers replace human repeatable work, image processing and pattern recognition are undoubtedly a hot topic in current research and application. At this year's National Committee of the Chinese People's Political Consultative Conference, Robin Li, member of the Chinese People's Political Consultative Conference and CEO of Baidu, suggested the establishment of the "Chinese Brain" plan to promote the leapfrog development of artificial intelligence and seize the commanding heights of a new round of technological revolution; Dedicated computer vision group. This shows that the era of artificial intelligence is coming, and image processing and pattern recognition, as an important part of artificial intelligence, the...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10132G06T2207/20084
Inventor 郝爱民闫德辉李帅秦洪
Owner BEIHANG UNIV
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