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Medical Image Processing Techniques for the Objective Quantification of Pathology in Magnetic Resonance Images of the Brain.

機譯：醫(yī)學(xué)圖像處理技術(shù)，用于對腦部磁共振圖像中的病理學(xué)進行客觀量化。

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摘要

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner.;FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model.;To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required.;Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient.;To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.
機譯：本文的研究重點是自動檢測腦液衰減反轉(zhuǎn)恢復(fù)（FLAIR）磁共振圖像（MRI）中的白質(zhì)病變（WML）。醫(yī)學(xué)界對WML的興趣與日俱增，因為顯示每位患者的WML總量（病變負荷）與未來的卒中以及頸動脈疾病有關(guān)。 WML的手動分段非常耗時，費力，依賴于觀察者并且容易出錯?？梢允褂米詣覹ML分割算法代替，因為它們以定量，高效，可重現(xiàn)和可靠的方式讓位于病變負荷計算; FLAIR MRI至少受兩種類型的退化影響，包括加性噪聲和部分體積平均（PVA） ）工件，這會影響自動化算法的準(zhǔn)確性。依賴高斯分布的基于模型的方法已被廣泛用于處理這兩種失真，但不適用于帶有WML的FLAIR。多線圈FLAIR MRI中的噪聲分布為非高斯分布，WML的存在以難以建模的方式修改了組織分布。為此，本論文提出了一種在存在噪聲的情況下對PVA偽影進行建模的新穎方法。該方法是一種通用的自適應(yīng)方法，已應(yīng)用于各種MRI加權(quán)（有無病理），以實現(xiàn)可靠的PVA定量和組織分割。不需要關(guān)于類分布的先驗假設(shè)，也不需要訓(xùn)練樣本或初始化參數(shù)。;使用模擬和真實的FLAIR MRI完成了分割實驗。使用真實的大腦和病理模型，模擬圖像產(chǎn)生了噪聲和PVA失真。真實圖像是從Sunnybrook健康科學(xué)中心獲得的，而WML地面真相是通過手動分割實驗生成的。發(fā)現(xiàn)模擬圖像和真實圖像的平均DSC分別為0.99和0.83。進行了病灶負荷研究，檢查了每位患者的半球間WML體積。為了顯示該方法的一般性，該提議的技術(shù)還用于無病理性的T1和T2 MRI。驗證研究表明，提出的框架可對PVA進行可靠的分類，并且對組織類別進行了細分，效果良好。

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  • 作者

    Khademi, April Ellahe.;

  • 作者單位

    University of Toronto (Canada).;

  • 授予單位 University of Toronto (Canada).;
  • 學(xué)科 Engineering Electronics and Electrical.
  • 學(xué)位 Ph.D.
  • 年度 2012
  • 頁碼 278 p.
  • 總頁數(shù) 278
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

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