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Symmetry and asymmetry analysis and its significance in neuro-imaging applications.

機譯：對稱性和不對稱性分析及其在神經成像應用中的意義。

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Advances in computer technologies over the last decade have catalyzed the development of modern computerized schemes for lesion detections in radiological images. One biggest challenge is that computers generally lack sufficient perceptibility and intelligence in terms of discovering pathological patterns; the decision making process is hence hindered. As it is known that knowledge plays an indispensable role in computer vision and artificial intelligence, integrating anatomical knowledge into such computer system holds great promise for facilitating decision making and improving patient care in neuro-radiology.; Based on the assumption that the brain exhibits a high level of bi-fold symmetry and that this symmetry is violated in the presence of pathological conditions, a principle goal of this effort was motivated to construct a symmetry-based paradigm for automatic localization and segmentation of brain lesions. The framework of this methodology is grounded on the hypothesis that the systematic correlation between asymmetry and pathologies can be a key to the improvement of existing detection algorithms. Integrating symmetry/asymmetry information as the prior knowledge or heuristics into a computer aided diagnostic (CAD) system, ought to enhance the system performance in the analysis of brain pathologies.; The methodology of this study is two-fold: First a symmetry axis or, the symmetry plane needs to be spatially oriented because it is valuable for the correction of possible misalignment of radiological scans and for hemisphere-wise asymmetry evaluation. In a second step automatic detection and quantification of brain lesions such as stroke and tumor is required. In this dissertation, I explore and discuss the discriminating capacity of symmetry/asymmetry in the context of extracting pathological findings in various radiological applications with different modalities, such as MRI and CT. In other words, the first part of this research focuses on solving an image registration problem, and the second part relies upon the performance of pattern recognition and segmentation algorithms applied to asymmetry detection.; It should be noted, however, that the methods developed in this thesis for a set of particular neuro-applications may have a more general applicability since many other parts of human body, not only limited to the brain, are highly symmetrical in nature.
機譯：在過去的十年中，計算機技術的進步推動了用于放射圖像中病變檢測的現代計算機化方案的發(fā)展。最大的挑戰(zhàn)是計算機在發(fā)現病理模式方面通常缺乏足夠的感知力和智能。因此阻礙了決策過程。眾所周知，知識在計算機視覺和人工智能中起著不可或缺的作用，將解剖學知識整合到這樣的計算機系統(tǒng)中，對促進決策制定和改善神經放射學的患??者護理具有廣闊的前景?；谶@樣的假設，即大腦表現出高水平的雙重對稱性，并且在存在病理狀況的情況下這種對稱性遭到破壞，因此，這一努力的主要目標是構建一種基于對稱性的范式，用于自動定位和分割腦部病變。該方法的框架基于以下假設：不對稱和病理之間的系統(tǒng)相關性可能是改進現有檢測算法的關鍵。將對稱性/非對稱性信息作為先驗知識或啟發(fā)式方法集成到計算機輔助診斷（CAD）系統(tǒng)中，應增強系統(tǒng)在腦部病理分析中的性能。這項研究的方法有兩個方面：首先是對稱軸，或者對稱平面必須在空間上定向，因為它對于糾正放射線掃描可能的不對準和對半球不對稱性評估非常有價值。在第二步中，需要對腦損傷（例如中風和腫瘤）進行自動檢測和定量。在本文中，我探討并討論了在具有不同模式的各種放射學應用（例如MRI和CT）中提取病理結果時，對稱性/非對稱性的區(qū)分能力。換句話說，本研究的第一部分側重于解決圖像配準問題，第二部分依賴于模式識別和分割算法在不對稱檢測中的性能。但是，應該注意的是，由于人體的許多其他部分（不僅限于大腦）在本質上都是高度對稱的，因此本文針對一組特定的神經應用開發(fā)的方法可能具有更廣泛的適用性。

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  • 作者

    Liu, Xin.;

  • 作者單位

    Columbia University.;

  • 授予單位 Columbia University.;
  • 學科 Engineering Biomedical.; Biology Bioinformatics.; Artificial Intelligence.
  • 學位 Ph.D.
  • 年度 2008
  • 頁碼 190 p.
  • 總頁數 190
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類 生物醫(yī)學工程;人工智能理論;
  • 關鍵詞

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