Lung texture recognition method based on deep neural network extraction appearance and geometric features

A technology of geometric features and recognition methods, applied in the field of medical image processing and computer vision, can solve the problems of inability to complete high-precision texture recognition, ignoring geometric features, etc., and achieve the effect of improving the recognition accuracy, easy construction, and easy implementation.

Inactive Publication Date: 2018-08-21
DALIAN UNIV OF TECH
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Problems solved by technology

The above-mentioned deep learning-based methods only mine the apparent information of the lung texture, ignoring its geometr

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  • Lung texture recognition method based on deep neural network extraction appearance and geometric features
  • Lung texture recognition method based on deep neural network extraction appearance and geometric features
  • Lung texture recognition method based on deep neural network extraction appearance and geometric features

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[0037] The present invention proposes a lung texture recognition method based on deep neural network extraction of apparent and geometric features, which is described in detail with reference to the drawings and embodiments as follows:

[0038] The present invention constructs a dual-channel residual network, uses lung CT images for training, and achieves a higher correct recognition rate in the test. The specific implementation process is as follows figure 1 As shown, the method includes the following steps;

[0039] 1) Prepare initial data:

[0040] 1-1) A total of 217 patients’ lung CT images were collected in the experiment. Among them, the CT images of 187 patients contained 6 typical textures of diffuse lung disease, namely nodular, emphysema, honeycomb, fixed, ground glass and lined ground glass textures; CT images of the remaining 30 patients In the image, only normal lung tissue texture is presented. Using these 217 CT images, 7 types of lung texture image patches (includi...

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Abstract

The invention discloses a lung texture recognition method based on deep neural network extraction appearance and geometric features, which belongs to the field of medical image processing and computervision. 217 lung 3D computed tomography (CT) images are used as source data, a plurality of groups of data sets are obtained through preprocessing, and each group of data comprises a CT image small block, a corresponding geometric information image small block and a category label. A dual-channel residual network framework is built, the CT image small block and the corresponding geometric information small block are used as input of each channel respectively, the appearance information and the geometric information of the lung textures are learnt respectively through the dual-channel residualnetwork, the information is effectively fused, and the recognition rate is thus high. Besides, the provided network has a distinct structure, and building and realization are easy.

Description

technical field [0001] The invention belongs to the field of medical image processing and computer vision, and relates to using a deep neural network framework to extract the relevant features of the appearance and geometric information of lung computed tomography (CT) images, and to classify different types of lung CT image textures , specifically relates to a lung texture recognition method based on deep neural network to extract appearance and geometric features. Background technique [0002] Diffuse lung disease is a general term for lung diseases that present widely distributed large areas of lung shadows in the lung area of ​​CT images. Because these lung shadow textures are complex and easily confused, it is difficult to accurately identify different lung textures even for experienced radiologists. Therefore, it is necessary to establish a computer-aided diagnosis (CAD) system for accurate and efficient automatic recognition of lung texture in CT images of diffuse lu...

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Application Information

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IPC IPC(8): G06T7/00G06T7/40G06N3/04
CPCG06T7/0012G06T7/40G06T2207/30061G06T2207/20081G06T2207/10081G06N3/045G06T2207/20084G06T7/41G06N3/08G06N3/04
Inventor 徐睿叶昕辰丛臻
Owner DALIAN UNIV OF TECH
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