基于学习的腰椎检测与跟踪方法研究

发布时间：2018-03-09 06:28

本文选题：腰椎　切入点：卷积神经网络　出处：《南京理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着数字图像技术与计算机视觉技术的不断发展,使用计算机技术手段对医学图像数据进行处理和分析来辅助医生诊断疾病已逐渐得到普及。本文针对腰椎不稳症这一普遍而严重的健康问题,提出了一种腰椎不稳症辅助诊断方法:基于学习的腰椎检测和跟踪方法。该方法使用腰椎的数字视频影像(DVF)进行处理与分析,主要研究工作如下:(1)针对目前腰椎跟踪方法中初始状态的腰椎目标均需通过人工标注的问题,提出了一种基于卷积神经网络的腰椎检测方法。首先对原始DVF图像进行对比度拉伸和去噪预处理,增加DVF图像的清晰度;在离线训练阶段,使用大量腰椎样本图像来训练卷积神经网络分类器;检测时,利用霍夫变换在二值化的DVF图像中寻找到腰椎的边角点以得到腰椎的角度参数,再使用腰椎解剖统计数据获得初始候选检测区域的边界框;之后按边界框从经预处理的DVF图像中提取初始候选检测区域送入卷积神经网络分类器当中获得检测结果。该方法在实验中表现出了十分高的腰椎检测准确率,能够实现对精度较严格的场景下的应用。(2)针对当前许多跟踪算法在对腰椎进行跟踪时的鲁棒性较差的问题,提出了一种可在线更新的基于栈式自动编码机的腰椎跟踪方法。在离线训练时,获得能够表述通用物体的深层特征;在线跟踪时,以粒子滤波为框架,使用自动编码机获得当前帧的跟踪结果;最后再对神经网络权值参数进行在线更新。该方法有效提高了算法的鲁棒性并减少了跟踪漂移的可能,在实验中展现出了很强的腰椎识别能力。以上技术能够在DVF影像对比度较低且较为模糊的情况下将部分腰椎检测出来,并展现出了很强的识别能力,在跟踪过程中可准确发现腰椎不稳症状,可作为腰椎不稳症临床诊断中非常有效的辅助手段。
[Abstract]:With the development of digital image technology and computer vision technology, The use of computer technology to process and analyze medical image data to assist doctors in diagnosing diseases has become increasingly popular. In this paper, an auxiliary diagnosis method of lumbar vertebrae instability is proposed, which is based on learning and tracking of lumbar vertebrae, which is processed and analyzed by digital video image of lumbar vertebrae (DVF). The main research work is as follows: (1) aiming at the problem that the initial status of lumbar vertebrae in the current lumbar tracking method needs manual labeling, A novel lumbar spine detection method based on convolution neural network is proposed. Firstly, the contrast stretching and denoising preprocessing of the original DVF image is carried out to increase the clarity of the DVF image. The convolutional neural network classifier is trained with a large number of lumbar vertebrae samples, and the angle parameters of the lumbar vertebrae are obtained by using Hoff transform in the binary DVF image to find the side corner of the lumbar vertebrae. The boundary frame of the initial candidate detection area was obtained by using lumbar anatomical statistical data. Then, according to the boundary frame, the initial candidate detection area is extracted from the pre-processed DVF image and sent to the convolutional neural network classifier to obtain the detection results. To solve the problem that many current tracking algorithms have poor robustness in tracking the lumbar vertebrae. In this paper, an on-line updating method of lumbar spine tracking based on stack automatic coding machine is proposed. When training offline, the deep features of common objects can be expressed, and the particle filter is used as the framework for on-line tracking. The tracking result of the current frame is obtained by using the automatic coding machine. Finally, the weights of the neural network are updated online. This method can effectively improve the robustness of the algorithm and reduce the possibility of tracking drift. These techniques can detect parts of lumbar vertebrae in the case of low contrast and blur of DVF image, and show strong recognition ability. The unstable symptoms of lumbar vertebrae can be found accurately in the process of tracking, and can be used as a very effective auxiliary method in the clinical diagnosis of lumbar instability.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;R445;R681.5

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