乳腺超声图像的全自动分割

发布时间：2021-05-12 02:13

　　乳腺癌是最常被发现的癌症之一,它威胁到女性的健康和生命。早期准确的疾病诊断在癌症治疗中起着重要作用。临床研究表明,如果在早期阶段检测到癌症,它可以相对容易地治愈而不会对患者造成太大伤害。超声成像是检测乳腺癌的一种方法。医疗超声波使用人体无法听到的高频声波（>20,000 Hz）,将脉冲发送到人体组织中并以不同的属性反射回来被记录并显示为图像,是一种观察人体内肿瘤和其他异常等疾病的便捷工具。本文的主要任务为减少癌症诊断中的错误,使医用计算机可以自动发现肿瘤区域及周围组织结构。实现此任务的一种方法是应用图像语义分割。图像语义分割不仅可以计算对象位置,还可以确定其所属类别。分类模型用于计算每个像素的概率分布,以优化结果进行准确分割。本文首先总结了国内外图像分割的研究现状,然后提出了图像语义分割方法的思路。这项研究的主要思想是应用全卷积神经网络进行特征提取。最初,FCN使用训练样本和单热图像进行训练,然后在提供另一组图像之后,可以生成分割结果。全卷积网络的架构主要采用U-net及其他两种变体,将通过实验分别测试三种架构的性能。这两个称为Dual U-net与Tight U-net的变体基... 

【文章来源】：哈尔滨工业大学黑龙江省 211工程院校 985工程院校

【文章页数】：130 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract (In English)
Chapter 1 Introduction
    1.1 Background
    1.2 Research inside and outside of country and analysis
        1.2.1 Overview of non-semantic segmentation methods
        1.2.2 Overview of semantic segmentation methods
    1.3 Problems of segmentation methods
    1.4 Research summary
    1.5 Organization of the thesis
Chapter 2 Fundamentals of Convolutional Neural Networks
    2.1 Image Augmentation
    2.2 Confusion matrix and Jaccard index
    2.3 Architecture of Convolution neural network
        2.3.1 Convolution layer
        2.3.2 Pooling Layer
        2.3.3 Fully Connected Layer
        2.3.4 Activation functions
    2.4 Original U-net Architecture
        2.4.1 Choosing the architecture of Fully Convolutional Network
        2.4.2 The description of the original architecture
        2.4.3 Re LU
    2.5 Training the network
        2.5.1 Loss function
        2.5.2 Optimization algorithm
    2.6 Wavelet Image Transformation
        2.6.1 DWT in two dimensions
        2.6.2 The application of DWT in the research
Chapter 3 Segmentation with Dual U-net
    3.1 Main Contribution
        3.1.1 U-net and Its Limitations
        3.1.2 Dual U-net
    3.2 Application of Original U-net and Dual U-net
    3.3 Training
    3.4 Experiments and results
        3.4.1 Two-class classification
        3.4.2 Multi-class classification
    3.5 Brief Summary
Chapter 4 Segmentation with Tight U-net
    4.1 Architecture
    4.2 Application of Tight U-net
    4.3 Training
    4.4 Experiments and results
        4.4.1 Two-class classification
        4.4.2 Multi-class classification
    4.5 Brief Summary
Chapter 5 Optimization of segmentation results of U-net with CRF
    5.1 Mean-field approach
    5.2 CRF application
    5.3 Experiments and results
        5.3.1 Two-class classification
        5.3.2 Multi-class classification
    5.4 Brief Summary
Conclusion (In English)
结论
References
Acknowledgement
Resume



本文编号：3182533