Fuzzy System Based Medical Image Processing for Brain Disease Prediction.
Fuzzy System Based Medical Image Processing for Brain Disease Prediction.
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DOI：
10.3389/fnins.2021.714318
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发表时间：
2021
影响因子：
4.3
通讯作者：
Lv Z
中科院分区：
文献类型：
--
作者：
Hu M;Zhong Y;Xie S;Lv H;Lv Z
本工作旨在探索基于模糊系统的医学图像处理的性能，用于预测脑部疾病。核磁共振（NMR）的成像机理和人脑组织的复杂性，使得脑部MRI（Magnetic Resonance Imaging）图像呈现出不同程度的噪声、弱边界和伪影。因此，对模糊聚类算法进行了改进。为了保证模型的安全性能，设计了一种基于改进模糊聚类和HPU-Net（Hybrid Pyramid U-Net Model for Brain Tumor Segmentation）的脑图像处理和脑疾病诊断预测模型。通过对某医院采集的脑MRI图像进行仿真实验，验证了算法的有效性。此外，CNN（卷积神经网络），RNN（递归神经网络），FCM（模糊C-均值），LDCFCM（局部密度聚类模糊C-均值），和AFCM（自适应模糊C-均值）包括在仿真实验的性能比较。实验结果表明，在同等条件下，与其他模型相比，该算法具有节点数多、能耗低、变化稳定等优点。从网络整体性能来看，该算法能够以最快的速度完成数据传输任务，平均基本维持在4.5 s左右，性能明显优于其他模型。进一步的预测性能分析表明，所提出的算法提供了最高的预测精度为整体肿瘤下DSC（骰子相似系数），达到0.936。此外，其Jaccard系数为0.845，证明了其上级分割精度优于其他模型。总之，该算法在保证能耗的前提下，能够提供比其他模型更高的准确率、更明显的去噪效果以及最佳的分割和识别效果。研究结果可为脑图像的特征识别和预测诊断提供实验依据。
The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.
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