基于深度学习与自适应对比度增强的臂丛神经超声图像优化

计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 236-240.

基于深度学习与自适应对比度增强的臂丛神经超声图像优化

杨桐^1,2, 张姗姗^1,2, 江方舟^1,2, 李奕飞^1,2, 俞戈昊^1,2, 赵地¹

(中国科学院计算技术研究所北京100190)¹;
(北京邮电大学北京100089)²

出版日期:2019-11-10 发布日期:2019-11-20
通讯作者: 赵地(1978－),男,博士,副研究员,CCF会员,主要研究方向为脑科学、大数据分析、机器学习,E-mail:zhaodi@escience.cn。
作者简介:杨桐(1995－),男,CCF会员,主要研究方向为大数据分析、图像处理、机器学习。
基金资助:
本文受北京市自然科学基金重点项目(4161004),北京市科技计划项目(Z171100000117001),北京市科技计划项目(Z161100000216143),国家重点研发计划项目(2018ZX10723203)资助。

Brachial Plexus Ultrasound Image Optimization Based on Deep Learning and Adaptive Contrast Enhancement

YANG Tong^1,2, ZHANG Shan-shan^1,2, JIANG Fang-zhou^1,2, LI Yi-fei^1,2, YU Ge-hao^1,2, ZHAO Di¹

(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)¹;
(Beijing University of Posts and Telecommunications,Beijing 100089,China)²

Online:2019-11-10 Published:2019-11-20

摘要/Abstract

摘要： 现代医学中,利用臂丛神经分割与识别后的图像经过对比度增强优化后,可以更利于医师识别出病症和肿瘤。在上肢手术与术后护理中需要进行麻醉护理,而臂丛神经阻滞是一种常用的局部麻醉方式。为了精确确定臂丛神经的位置,在实际治疗过程中广泛应用超声设备对神经系统进行检测和定位。文中阐述了基于深度学习以及神经网络,在超声动态影像中准确地识别并分割臂丛神经,且在截出的图像中通过自适应对比度增强来优化超声图像的显示。实验数据来自于北京积水潭医院,分为病人的超声影像以及对应的良性恶性肿瘤图片。文中运用了增强对比度的算法对所提取的特征进行处理,结果表明增强了图像的对比度和显示内容的精确度。

关键词: 臂丛神经, 超声图像, 卷积神经网络, 深度学习, 图像处理, 自适应增强对比度算法

Abstract: In modern medicine,the image of the brachial plexus segmentation and recognition is optimized by contrast enhancement to help the physician identify the disease and tumor.Brachial plexus block is a commonly used method of local anesthesia in upper limb surgery and postoperative care.In order to accurately determine the position of thebrachialplexus,the hospital extensively applies ultrasound equipment to detect and locate the nervous system.This paper described the accurate recognition and segmentation of brachial plexus in ultrasound dynamic images based on deeplear-ning and neural network,and optimized the display of ultrasound images through adaptive contrast enhancement in the cut-out images.The experiment data come from the Beijing Jishuitan Hospital,which are divided into ultrasound images of patients and corresponding pictures of benign malignancies.The enhanced contrast algorithm was used to process the extracted features.The experimental results show that this algorithm enhances the contrast of the image and the accuracy of the displayed content.

Key words: Adaptive enhancement contrast algorithm, Brachial plexus, Convolution and neural network, Deep learning, Image process, Ultrasound image

中图分类号:

TN399

杨桐, 张姗姗, 江方舟, 李奕飞, 俞戈昊, 赵地. 基于深度学习与自适应对比度增强的臂丛神经超声图像优化[J]. 计算机科学, 2019, 46(11A): 236-240. https://doi.org/

YANG Tong, ZHANG Shan-shan, JIANG Fang-zhou, LI Yi-fei, YU Ge-hao, ZHAO Di. Brachial Plexus Ultrasound Image Optimization Based on Deep Learning and Adaptive Contrast Enhancement[J]. Computer Science, 2019, 46(11A): 236-240. https://doi.org/

参考文献

[1]金烈烈.臂丛神经的超声影像学研究[J].中华手外科杂志,2007,23(4):248-251.
[2]吴道珠.高频超声对臂丛神经显像和定位的价值[J].中华超声影像学杂志,2006,15(6):449-452.
[3]CASATI A,DANELLI G,BACIARELLO M,et al.A prospective randomized comparison between ultrasound and nerve stimulation guidance for multiple injection axillary brachial eels block[J].Anesthesiology,2007,106:992-996.
[4]劳杰,何继银,顾玉东,等.创伤性臂丛神经损伤合并肩袖及肱二头肌长头腱损伤的超声诊断[J].中华手外科杂志,2006,22:l44-l45.
[5]曾炜,王弘士,朱世亮,等.颈部颈丛和臂丛神经鞘瘤的彩色多普勒超声定位诊断[J].中国超声医学杂志,2000,16:819-822.
[6]李丽玮.基于自适应对比度增强和深度 CNN 的脂肪肝 B 超影像诊断[J].计算机应用,2018(S2).
[7]BENGIO Y S.Learning Deep Architectures for AI[J].Machine Learning,2009,2(1):1-127.
[8]王振,高茂庭.基于卷积神经网络的图像识别算法设计与实现[D].上海:上海海事大学,2015.
[9]柴睿,林岳,杨桐.基于SegNet模型的臂丛神经超声图像分割[J].计算机工程,2017.
[10]李彦冬,郝宗波,雷航.卷积神经网络研究综述[J].计算机应用,2016,36(9):2508-2515,2565.
[11]朱其刚,朱志强.基于自适应邻域灰度直方图均衡的超声内窥镜图像增强[J].山东科技大学学报(自然科学版),2004,23(3).
[12]张志龙,李吉成,沈振康.一种保持图像细节的直方图均衡新算法[J].计算机工程与科学,2006,28(5):36239.
[13]上官伟,李金.综合运用灰度变换方法改善超声医学图像质量[J].应用科技,2005,32(11):40243.
[14]刘丽娜.超声定量技术诊断弥漫性脂肪肝的研究[D].广州:南方医科大学,2014.
[15]康伟.脂肪肝医学超声图像定量分析的研究[D].大连:大连理工大学,2007.

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