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研究生: 楊孟銓
Meng-Cyue Yang
論文名稱: 自動化計算腰椎間盤突出患者椎管狹窄程度與手術準則關係之研究
Automatically Calculate the Degree of Spinal Stenosis in Patients with Lumbar Disc Herniation and Study on the Relationship Between Criteria of Surgery Treatment
指導教授: 郭中豐
Chung-Feng Kuo
口試委員: 劉林肯
Lincoln Liu
黃昌群
Chang-Chiun Huang
邱智瑋
Chih-Wei Chiu
學位類別: 碩士
Master
系所名稱: 工程學院 - 材料科學與工程系
Department of Materials Science and Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 中文
論文頁數: 139
中文關鍵詞: 椎間盤突出椎管狹窄影像處理電腦輔助偵測區域限制型主動輪廓法支持向量機三維重建
外文關鍵詞: Disc Herniation, Spinal Stenosis, Image Processing, Computer Aided Detection, Improved Active Contour Method, Support Vector Machine, 3D-reconstruction
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  • 腰椎間盤突出症會造成椎管狹窄,是成年人神經根損傷最常見的原因。目前治療椎間盤突出症的方式主要分為手術治療及保守治療,然而是否需要進行手術治療並沒有一定的標準,文獻指出進行保守治療六週後病患症狀沒有明顯好轉,臨床醫師將建議病患進行手術治療。為了避免評估療效造成病患時間以及醫療資源的浪費,本研究提出一套自動偵測突出椎間盤病計算狹窄程度的系統,運用於腰部T2加權以及脂肪訊號抑制之核磁共振影像上,過程分別是影像前處理、椎間盤提取、突出判斷、椎管區域提取、重建與檢視。因為椎間盤的灰階值與肌肉、軟骨等組織非常相近,在椎間盤提取的部分提出了區域型主動輪廓法,運用限制圈選邊界的方式以得到更加精準的椎間盤輪廓。為了快速判斷出突出椎間盤,本研究使用支持向量機對椎間盤紋理與平均灰度值特徵進行分類。因為脂肪訊號抑制核磁共振影像中水分呈現白色,利用此特性迅速的將椎管提取出來,圈選突出的區域,得到椎管與椎間盤突出部位後進行三維重建。
    本研究採用532位椎間盤突出病患的核磁共振影像進行實驗與評估,所提出的系統能自動計算狹窄程度並進行三維重建,每個樣本經過影像濾波、椎間盤圈選、突出辨識、椎管提取以及三維重建後,平均需要55秒進行計算,系統的準確率為96.42%。本研究之系統能夠輸出腰椎間盤突出部位三維重建模型以及椎管狹窄程度的量化數值,有助於臨床醫師的診斷與後續療程規劃。


    Lumbar disc herniation will causes spinal stenosis, and it’s also the most common cause of nerve root injury in adults. At present, the methods for treating disc herniation are mainly divided into surgical treatment, and conservative treatment. However, there are no definite criteria for assessing whether or not it is necessary to perform surgery, in general, there is no obvious improvement after six weeks of conservative treatment. The medical staff will recommend the patient for surgical treatment. In order to avoid wasting patient’s time and medical resources while evaluation the efficacy of conservative treatment, this research proposes a set of image processing methods applied to spinal canal and intervertebral disc herniation in lumbar MRI image for calculating the degree of spinal and three-dimensional reconstruction. The process are image preprocessing, disc extraction, herniated disc judgment, spinal region extraction and three-dimensional reconstruction. Because the gray value of the disc is very similar to the surrounding tissue, an improved active contour method is proposed in the disc extraction part to obtain a more accurate disc contour. In order to quickly determine the herniated intervertebral discs, the support vector machine was used to classify the features of intervertebral discs such as texture features and average gray values. Because the moisture in the MR image of fat signal suppression is white, this feature can be used to quickly extract the spinal canal and find out the prominent areas to obtain the position of the spinal canal and disc herniation and commencing three-dimensional reconstruction.
    In this study, 532 MR images of patients with disc herniation were examined and evaluated. The proposed system can automatically calculate the degree of stenosis and perform three-dimensional reconstruction, each sample takes 55 seconds to calculate, the accuracy of the system is 96.42%. The system of the this study can output three-dimensional reconstruction models and quantified values of spinal stenosis degree, which is helpful for the diagnosis of clinicians.

    摘要 I Abstract II 致謝 IV 目錄 V 圖索引 IX 表索引 XII 第1章 緒論 1 1.1 研究動機 1 1.2 文獻回顧 3 1.2.1 椎間盤病灶檢測 3 1.2.2 椎間盤與椎管影像圈選 4 1.2.3 椎間盤突出特徵選取 5 1.2.4 病患手術建議指標 6 1.3 研究目的 8 1.4 論文架構 9 第2章 相關醫學簡介與研究設備 11 2.1 脊椎構造 11 2.2 腰椎間盤突出症 12 2.2.1 椎間盤突出的定義 12 2.2.2 椎間盤突出種類 12 2.2.3 椎間盤突出的位置 13 2.2.4 治療方式 14 2.3 椎間盤突出症的開刀標準 14 2.4 研究設備 15 2.4.1 醫學影像擷取系統 15 2.4.2 軟硬體介紹 15 2.5 研究樣本 16 第3章 影像相關理論 18 3.1 低通濾波器 18 3.1.1 平均濾波器 18 3.1.2 中值濾波器 19 3.1.3 韋納濾波器 19 3.2 影像增強 21 3.2.1 對比度自適應直方圖等化 21 3.2.2 輪廓光源校正 22 3.2.3 索貝爾邊緣檢測 24 3.4 影像分割 24 3.4.1 Otsu’s演算法 25 3.4.2 K-means演算法 27 3.4.3模糊C均值演算法 28 3.4.4 距離轉換 30 3.4.5 分水嶺分割 31 3.5形態學 33 3.5.1標記法 34 3.5.2侵蝕 34 3.5.3膨脹 35 3.5.4斷開 36 3.5.5閉合 36 3.5.6補洞法 37 3.5. 7細線化 38 3.6 輪廓提取理論 38 3.6.1 主動輪廓法 39 3.6.2 區域限制型輪廓提取法 41 3.7 影像特徵值 42 3.7.1 平均灰階值 43 3.7.2 影像熵值 43 3.7.3 灰階共生矩陣 43 3.8 支持向量機 46 3.9 K次交叉驗證 50 3.10 三維重建 51 3.11 醫學指標分析方法 54 第4章 椎管狹窄程度自動計算系統 57 4.1 影像前處理 58 4.2 椎間盤提取 60 4.3 突出判斷 64 4.3.1 椎管體積計算邊界 69 4.4椎管區域提取 71 4.5重建與檢視 73 4.5.1 三維重建 74 4.5.2 狹窄程度計算 78 4.5.3 突出形式判斷 78 第5章 實驗結果 80 5.1 區域限制型主動輪廓法之優越性 80 5.2 系統模型之優越性 82 5.3 突出方向之顯著性 84 5.4 突出位置之顯著性 85 5.5醫學指標分析 86 5.6 實驗結果 90 5.7 實驗誤差分析 91 第6章 結論 93 參考文獻 95 附錄:樣本資訊 102 簡寫對照表 124

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