Medical image denoising using convolutional neural network: a residual learning approach
Medical image denoising using convolutional neural network: a residual learning approach
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使用卷积神经网络进行医学图像去噪：一种残差学习方法
DOI：
10.1007/s11227-017-2080-0
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发表时间：
2019-02-01
影响因子：
3.3
通讯作者：
Liu, Shaohui
中科院分区：
文献类型：
--
作者：
Jifara, Worku;Jiang, Feng;Liu, Shaohui
在医学成像中，去噪对于图像分析、疾病诊断和治疗都具有重要意义。目前，基于深度学习的图像去噪方法是有效的，但由于训练样本量的要求，这些方法受到了限制（即对于小数据量来说不够成功）。在小样本条件下，通过研究医学图像去噪的深度框架模型、学习方法和正则化方法，设计深度前馈去噪卷积神经网络。更具体地说，我们在深度模型中使用残差学习作为学习方法，批归一化作为正则化。残差学习方法不像大多数其他图像去噪方法直接学习潜净图像，而是从有噪声的图像中学习噪声，通过从有噪声图像中减去学习到的残差得到去噪图像。此外，将批归一化与残差学习相结合，提高了模型的学习精度和训练时间。我们用标准图像质量指标、峰值信噪比和结构相似度来计算重建或去噪图像的质量，并将我们的模型性能与一些医学图像去噪技术进行比较。实验结果表明，该方法比其他方法具有更好的性能。
In medical imaging, denoising is very important for analysis of images, diagnosis and treatment of diseases. Currently, image denoising methods based on deep learning are effective, where the methods are however limited for the requirement of training sample size (i.e., not successful enough for small data size). Using small sample size, we design deep feed forward denoising convolutional neural networks by studying the model in deep framework, learning approach and regularization approach for medical image denoising. More specifically, we use residual learning as a learning approach and batch normalization as regularization in the deep model. Unlike most of the other image denoising approaches which directly learn the latent clean images, the residual learning approach learns the noise from the noisy images instead of the latent clean images where the denoised images are obtained by subtracting the learned residual from the noisy image. Moreover, batch normalization is integrated with residual learning to improve model learning accuracy and training time. We compute the quality of the reconstructed or denoised image in standard image quality metrics, peak signal to noise ratio and structural similarity and compare our model performance with some medical image denoising techniques. Experimental results reveal that our approach has better performance than some other methods.