Free interaction-type GrabCut tongue segmentation method based on deep learning

A deep learning and interactive technology, applied in the direction of image analysis, image data processing, instruments, etc., can solve the problems of weak automation performance and achieve the effect of improving the degree of automation

Inactive Publication Date: 2018-06-01
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0011] In order to overcome the problem that the existing CrabCut algorithm requires manual interaction during the use of tongue segmentation, given the segmentation frame of the foreground and background, the automatic performance is not strong, the present invention proposes an interactive-free GrabCut tongue based on deep learning Segmentation method, build a deep convolutional neural network to automatically locate the tongue body, so as to obtain the foreground and background segmentation frame, without manual setting, which improves the automation of the segmentation algorithm

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  • Free interaction-type GrabCut tongue segmentation method based on deep learning
  • Free interaction-type GrabCut tongue segmentation method based on deep learning
  • Free interaction-type GrabCut tongue segmentation method based on deep learning

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[0052] The present invention will be further described below in conjunction with the accompanying drawings.

[0053] refer to Figure 1 to Figure 4 , an interactive-free GrabCut tongue segmentation method based on deep learning, including a deep convolutional neural network for overall tongue feature extraction, a region-of-interest localization network for preliminary detection of tongue regions, and a region-of-interest localization network for A deep convolutional neural network for deep abstract feature extraction of regions, and a GrabCut algorithm for segmenting tongue images;

[0054] The deep convolutional neural network used for tongue overall feature extraction, as the basic network of the entire network model, is divided into five layers, a deep structure consisting of convolutional layers, activation layers and pooling layers alternately, implicitly Perform unsupervised learning from given tongue image data, avoiding manual explicit feature extraction;

[0055] T...

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Abstract

The invention discloses a free interaction-type GrabCut tongue segmentation method based on deep learning, which comprises a deep convolution neural network for extracting whole tongue features, a region of interest positioning network for initially detecting a tongue area, a deep convolution neural network for carrying out deep abstract feature extraction on the region of interest and a GrabCut algorithm for segmenting a tongue picture. The problem that the existing GrabCut algorithm relies too much on manual interaction in the case of tongue segmentation can be effectively solved, and the automatization of the GrabCut algorithm in tongue segmentation can be improved.

Description

technical field [0001] The invention relates to a segmentation method, in particular to the application of technologies such as TCM tongue diagnosis, computer vision, digital image processing, pattern recognition, deep learning and deep convolutional neural network in the field of automatic tongue image segmentation. Background technique [0002] Tongue diagnosis is an important part of medical inspection in China. Based on the observation of the coating on the patient's tongue and the properties of the tongue quality, including color and shape, it is possible to determine the location of the disease and then treat it based on syndrome differentiation. Nowadays, the standardized, quantitative and objective research of TCM tongue diagnosis has become the main research direction of the modernization of TCM diagnostics, which has extremely far-reaching significance for the development of TCM as a whole. [0003] The standardized, quantitative and objective research of tongue di...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/162G06T7/194
CPCG06T7/162G06T7/194G06T2207/20072G06T2207/20081G06T2207/20084G06T2207/20132
Inventor 王丽冉汤一平何霞陈朋袁公萍金宇杰
Owner ZHEJIANG UNIV OF TECH
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