Thyroid ultrasound image nodule analysis method based on deep learning network and shallow texture feature fusion

A deep learning network, thyroid nodule technology, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as heavy workload, high feature dimension, and insufficient depth features to accurately describe the thyroid gland, and reduce pain. and stress, improve work efficiency

Inactive Publication Date: 2019-09-06
NORTHEASTERN UNIV
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AI Technical Summary

Problems solved by technology

However, the traditional method requires manual design of complex feature extraction methods, the extracted feature dimensions are high, the workload is large and the efficiency is low
Since ordinary networks are not sensitive to the features of ultrasound

Method used

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  • Thyroid ultrasound image nodule analysis method based on deep learning network and shallow texture feature fusion
  • Thyroid ultrasound image nodule analysis method based on deep learning network and shallow texture feature fusion
  • Thyroid ultrasound image nodule analysis method based on deep learning network and shallow texture feature fusion

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Embodiment 1

[0013] A nodule analysis method for thyroid ultrasound images based on deep learning network and shallow texture feature fusion, such as figure 1 with figure 2 As shown, the method includes the following steps:

[0014] For the input thyroid ultrasound image, it needs to be preprocessed before feature extraction and classification to reduce the influence of manual intervention; in the ultrasound image preprocessing stage, the scale of texture details in ultrasound images from different sources is different, which will greatly Affects the learning of subsequent image features, so it is necessary to perform scale registration on the images so that the images in the training set have the same distance scale; perform binarization on the ultrasound images, mark the position of the suspected scale, and calculate the self-alignment of each column of pixels Correlation coefficient, and find the position of the extremum point that meets the conditions in this column, that is, the pos...

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Abstract

The invention relates to a thyroid ultrasound image nodule analysis method based on a deep learning network and shallow texture feature fusion, and belongs to the field of computer thyroid ultrasoundimage auxiliary analysis. The method comprises the following steps: preprocessing a thyroid ultrasound image; performing deep feature extraction based on a GooLeNet deep learning network; shallow texture feature extraction based on a rotation invariance local binary pattern; fusing the depth features and the shallow features; and classifying thyroid ultrasound images based on a cost-sensitive random forest. The thyroid nodule benign and malignant classification method can rapidly and accurately classify thyroid nodules by establishing the thyroid ultrasound image nodule analysis method.

Description

technical field [0001] The invention relates to a thyroid ultrasound image nodule analysis method based on deep learning network and shallow texture feature fusion, and belongs to the field of computer-assisted analysis of thyroid ultrasound images. Background technique [0002] In thyroid ultrasound images, the analysis of benign and malignant thyroid nodules is of great significance for the early diagnosis of thyroid cancer. With the development of medical imaging, most of the early thyroid nodules can be accurately detected in ultrasound images, but there is still a lack of accurate judgment on the nature of the nodules. Because most nodules are benign or inert, accurate judgment of benign and malignant nodules can reduce patients' medical risks and a large number of medical and health costs caused by acupuncture testing. Therefore, how to achieve a more accurate early diagnosis of benign and malignant thyroid nodules by ultrasonic images, avoid unnecessary acupuncture o...

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Application Information

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IPC IPC(8): G06T7/00G06T7/30G06T5/00
CPCG06T5/005G06T7/0012G06T2207/10132G06T2207/20081G06T2207/20084G06T7/30
Inventor 吴成东张艺菲迟剑宁
Owner NORTHEASTERN UNIV
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