- 學位論文
電腦輔助ASPECTS系統於急性缺血性腦中風病人之研究
Study of Computer Aids ASPECTS System in Acute Ischemic Stroke Patient
摘要
近年腦中風死亡率雖然有下降趨勢，但好發率仍然持續攀升。在臨床判讀醫師定義中風區域和範圍不大一致，因此本研究開發一套於缺血性中風電腦輔助偵測系統，協助醫生有效判斷腦中風嚴重程度，於有限時間內施打有效靜脈血栓溶解劑。 本研究透過影像處理技術建構系統。首先以適應性中值濾波器濾除於非增強CT影像之雜訊，並以二值化與區域成長法取得有效CT及MRI影像資訊，藉由紋理參數分析達自動圈選出缺血性區域輪廓，最後再比對腦部左、右兩側計算ASPECTS score及面積。本研究使用CT及MRI各100組8切面之影像(排除腫瘤、水腫、出血之影像)為樣本，其中40組影像作為訓練組60組為測試組。最終以MRI判讀之結果為依據，由醫師以肉眼判讀CT影像之ASPECTS score，判斷中風位置面積與本系統之判定結果做比較，並進行系統之效能評估與驗證。 已發展好之系統包括DICOM、BMP影像讀取、參數調整、病徵圈選及可疑區域的辨識等功能。在發展中發現，利用相關性(autocorrelation)、變異數(variance)、最大機率值(maximum probability)、同質度(Homogeneity)之紋理參數來分析辨別CT影像缺血性區塊之有無可得較佳之結果，訓練組於ROC曲線下面積(AUC)分別以六個紋理參數和四個紋理參數進行分析結果為0.952、0.942;藉由系統整體統計ASPECTS後訓練組與測試組之準確度、敏感度、特異性及Kappa值分別為0.93、0.71、0.98、0.72；0.90、0.76、1、0.52，顯示其系統評估結果優於臨床醫師。若樣本能先做合理之過濾，則ROC曲線於訓練組和測試組AUC可增加至0.974、0.975，而ASPECTS大於7需rt-PA治療之評估於訓練組和測試組準確率確實增加至1.000、0.9545。另外，藉系統自動偵測並計算缺血區域之面積，其面積與MRI影像及醫師進行重疊率之比對，由結果顯示系統訓練組與測試組重疊率可達0.77、0.66較優於其他四位醫師。 由結果顯示本研究系統可以幫助醫師更快速及準確的對病理判斷，並提供客觀的資訊給經驗較不足的醫師進行診斷，但本系統針對左、右腦皆有缺血性中風CT影像及舊的缺血區域使用上較有限制，期望未來能夠針對左、右皆含有缺血性中風與舊缺血區域等參數加設為系統判讀之項目。
並列摘要
Stroke mortality rate is a downward trend, but probability of occurrence still growing up in recent years. In clinical practice, the interpretation of physician to define the area and scope of stroke are not consistent. Thus, in this study a computer-aided detection system for ischemic stroke diagnosis was developed to help physician effectively determine the seriousness level of ischemic and giving effective dose of rt-PA treatment in limit time. This system was constructed based on image processing technology. First of all, an adaptive medium filter, bi-level, and regional growth methods were used to de-noise and obtain effective image information for brain CT image, respectively. And then the ischemic contour and area can be obtained by using the selection of texture parameters and automatic segmentation. Finally, by compared the left brain image with right side image and using the support the vector machine (SVM) as classification tool to judge region of area (ROI) belongs to normal or abnormal, and then to calculate ASPECTS score. The CT and MRI images of 100 sets (each set including 8 slice images and exclude cases with tumor, edema and hemorrhage) were used in the study. Based on the interpretation results from 40 sets of MRI, the corresponding CT images sets were used to train this system to determine the ASPECTS score and the stroke location area. The others 60 sets were used to test. Moreover, the results also compared with physician for the system performance evaluation and validation. Functions of image reading, parameter adjustment, disease identification, and identification of suspicious areas are included in the platform of this developed system. The AUC (area under curve) value of ROC for using four parameters (autocorrelation, variance, maximum probability, and Homogeneity) only and six texture parameters are 0.952 and 0.942 respectively for the training set image. According to the results of decision matrix, the accuracy, sensitivity, specificity, and Kappa value of the system are 0.9250, 0.7143, 0.9697, and 0.7248 for testing data set are 0.90, 0.76,1 and 0.52, respectively. By applied prescreening image step, the system performance is much better. The accuracy, sensitivity, specificity, and Kappa value of this system can be reach to 0.9730, 0.9286, 1.0000, and 0.9417, respectively. Moreover, the overlap ratio of ischemic area detected through this system and in MRI images are up to 0.77 and 0.66 for training group and test group. The results show that the system better than the other four physician groups. In the preliminary result of this study, we found that the use of autocorrelation, variance, maximum probability, homogeneity of the texture parameter analysis is better to identify the presence of ischemic blocks for CT images from the AUC for ROC curve. We also found that with/out prescreening the performance of system is better than conventional methods from the result of decision matrix. These results confirmed the practicality of this developed system and some other parameters may need to add in to improve the performance of this system in the future.