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Ultrasonic image intelligent segmentation method based on automatic context and data enhancement

An ultrasound image and context technology, applied in the fields of digital image processing and deep learning, can solve problems such as expensive and difficult ultrasound images, and achieve the effect of improving accuracy and reliability, and improving robustness and generalization.

Active Publication Date: 2018-08-28
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0010] At the same time, because deep learning often requires a large number of training samples, it is difficult and expensive to obtain large-scale ultrasound images, especially those labeled by professional technicians.

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  • Ultrasonic image intelligent segmentation method based on automatic context and data enhancement
  • Ultrasonic image intelligent segmentation method based on automatic context and data enhancement
  • Ultrasonic image intelligent segmentation method based on automatic context and data enhancement

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Embodiment Construction

[0070] The invention organically combines methods such as deep learning and digital image processing, and realizes intelligent segmentation of ultrasonic image defects based on automatic context and data enhancement. The present invention will be described in further detail below in conjunction with specific implementation steps and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0071] figure 1 It is a specific embodiment of the present invention, mainly including four modules of preprocessing, data enhancement, defect segmentation based on automatic context, and postprocessing. The present invention first performs a series of preprocessing on the ultrasound image data set. Then, data augmentation is performed on the preprocessed data set to expand the scale of the data set to obtain the augmented data set. Then, the augmented ultrasound dataset is fed into an automatic context-based fully convolutional neural network to train...

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Abstract

The invention relates to an ultrasonic image intelligent segmentation method based on the automatic context and data enhancement. The method comprises steps that firstly, a series of image pre-processing is carried out for an ultrasonic image data set to acquire a data set after pre-processing; secondly, data enhancement for the data set after pre-processing is carried out, and the data set scaleis expanded to acquire an amplified data set; thirdly, the amplified data set is inputted to the convolutional neural network based on the automatic context, a model is trained in an end-to-end mode,and preliminary segmentation of the amplified data set is realized; and lastly, refinement post-processing on the preliminary segmentation result is carried out. The method has advantages of high segmentation accuracy, strong robustness and generalization and good segmentation edge smoothness, and the ideal segmentation effect can be acquired under the condition of limited training data sets.

Description

technical field [0001] The method relates to technical fields such as digital image processing and deep learning, and specifically relates to an intelligent segmentation method of ultrasound images based on automatic context and data enhancement. Background technique [0002] With the development of modern industry, people pay more and more attention to material performance and product quality, and higher requirements are put forward for the detection accuracy and reliability of industrial products. Defect detection of industrial products not only detects the position of solid internal and surface defects, but also determines the size, type and shape of defects. Using ultrasonic pulse echo technology to detect defects not only has a long detection period and a high false detection rate, but also cannot quantitatively analyze the characteristics of defect shape and size. The development of ultrasonic imaging technology has enabled ultrasonic non-destructive testing to be ima...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11
CPCG06T7/0004G06T7/11
Inventor 韦岗梁舒马碧云李增
Owner SOUTH CHINA UNIV OF TECH
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