Automatic Brain Magnetic Resonance Image Denoising Using Texture Feature-Based Artificial Neural Networks

在磁振影像的分析上，雜訊一直是影像品質惡化的主要原因之一。影像變質不僅影響視覺上的臨床診斷，並會更進一步影響組織分類、影像分割和影像對位等電腦化處理結果。因此，去除腦部磁振影像的雜訊對於隨後眾多的影像處理分析及應用是相當重要的。然而，大部分去雜訊的演算法需要繁瑣費力的參數調整，且常對腦部影像的特徵和紋理相當敏感。因此，藉由人工智慧技術來自動化參數調整將會比較有效率。本論文使用磁振影像的紋理特徵資料，結合類神經網路訓練出一個可預測參數之模型，使用此模型得到最佳化雙邊濾波參數值，進而自動化去除腦部磁振影像中的雜訊。其中所使用的紋理特徵包含灰階共生矩陣、灰階連續長度矩陣、田村紋理特徵等方法。我們使用循序前進浮動選取演算法搭配t檢定方法選取最佳的特徵組合，再將此組合與倒傳遞類神經網路結合，完成網路模型之訓練及建立。我們使用一系列T1權重的模擬磁振影像以及臨床影像來測試此一自動化去雜訊系統，實驗結果顯示本研究所提的方法能準確地預測參數，並且自動化去除磁振影像中的雜訊，最後獲得的重建影像有相當不錯的品質。Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In our approach, a large number of image attributes are extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. Based on the t-test and the sequential forward floating selection (SFFS) methods, the optimal texture features are selected and incorporated into a back propagation neural network system. We have used a wide variety of simulated T1-weighted MR images and clinical images to evaluate the proposed automatic denoising system. Experimental results indicated that the proposed method accurately predicted the bilateral filtering parameters and automatically removed the noise in a number of MR images with satisfactory quantity and quality