提出特征滤波器CFF，降低心脏核磁图像分割中的卷积特征噪声。
Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation
- 将卷积特征视为高斯分布信号，设计低幅值通过的滤波器
- 在两个数据集上验证，特征信号信息熵显著下降
- 适合需要提升分割精度的医学图像分析研究者
噪声抑制是数字信号处理中的关键步骤，但在分割网络中常被忽略。本研究将卷积特征建模为服从高斯分布的特征信号矩阵，提出一种简单有效的特征滤波器——卷积特征滤波器（CFF），该滤波器本质上是低幅值通滤波器，旨在减少特征输入中的噪声。在两个主流2D分割网络和两个公开心脏磁共振图像数据集上进行了实验，结果表明特征信号矩阵中的噪声得到有效降低。为实现对降噪效果的数值评估，设计了一种二值化公式用于计算特征信号的信息熵。
原文摘要 · Abstract (English)
Noise reduction constitutes a crucial operation within Digital Signal Processing. Regrettably, it frequently remains neglected when dealing with the processing of convolutional features in segmentation networks. This oversight could trigger the butterfly effect, impairing the subsequent outcomes within the entire feature system. To complete this void, we consider convolutional features following Gaussian distributions as feature signal matrices and then present a simple and effective feature filter in this study. The proposed filter is fundamentally a low-amplitude pass filter primarily aimed at minimizing noise in feature signal inputs and is named Convolutional Feature Filter (CFF). We conducted experiments on two established 2D segmentation networks and two public cardiac MR image datasets to validate the effectiveness of the CFF, and the experimental findings demonstrated a decrease in noise within the feature signal matrices. To enable a numerical observation and analysis of this reduction, we developed a binarization equation to calculate the information entropy of feature signals.
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