Nonlinear Dimensionality Reduction of Brain MRI Data for Classification Applications

用于分类应用的脑 MRI 数据的非线性降维

• 批准号: 402202-2012
• 负责人: Tam, Roger
• 金额: $ 1.31万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=529468
• 关键词: Nonlinear; Dimensionality; Reduction; Brain; MRI

Classification, or the categorization of images into meaningful groups, is an important application in brain image analysis. Dimensionality reduction is a key component of many classification algorithms and is essential for reducing the complexity of mathematical methods for working with brain images. A simple example of dimensionality reduction in everyday use is the projection of 3D geography onto 2D maps. Different projection methods preserve different properties of the geography, just like different dimensionality reduction methods preserve different properties of brain images. Recently, groundbreaking discoveries in the field of machine learning have rekindled a strong interest in a type of neural network called the deep belief net (DBN), which has been shown to be very effective at automatically learning patterns of similarity in large groups of small images by reducing the dimensionality of the images to form a simplified virtual landscape in which similar images occupy the same valleys and dissimilar images are separated by hills. This approach has been shown to be more accurate for identifying faces and handwritten digits than practically all other methods. In comparison to the previous data used for DBN experiments, brain images are of much higher dimension and have much more complex shape and intensity variations. Our long-term research goal is to develop new DBN methods that can be used to form simplified landscapes of groups of brain images such that the valleys represent meaningful similarities that can be used for classification. Using a very large database of magnetic resonance images (MRIs) of patients with multiple sclerosis, we will develop new methods for making DBNs work with large, complex images. We will investigate what types of similarities DBNs are capable of extracting from groups of brain images, and determine whether those similarities are potentially useful for classifying brain images into various types of categories that will range from the visually obvious to ones that are not image-derived, such as clinical outcomes. If successful, this work will greatly benefit researchers in neuroimaging by allowing them to make much more effective use of their data.

分类，或者将图像分类为有意义的组，是大脑图像分析中的一个重要应用。降维是许多分类算法的关键组成部分，对于降低处理大脑图像的数学方法的复杂性至关重要。日常使用中降维的一个简单例子是将 3D 地理投影到 2D 地图上。不同的投影方法保留了地理的不同属性，就像不同的降维方法保留了大脑图像的不同属性一样。最近，机器学习领域的突破性发现重新点燃了人们对一种称为深度信念网络（DBN）的神经网络的浓厚兴趣，该网络已被证明能够非常有效地自动学习大组小图像中的相似模式，通过降低图像的维数来形成简化的虚拟景观，其中相似的图像占据相同的山谷，不同的图像被山丘隔开。事实证明，这种方法在识别人脸和手写数字方面比几乎所有其他方法更准确。与之前用于 DBN 实验的数据相比，大脑图像的维度要高得多，并且形状和强度变化要复杂得多。我们的长期研究目标是开发新的 DBN 方法，可用于形成大脑图像组的简化景观，使山谷代表可用于分类的有意义的相似性。利用多发性硬化症患者的磁共振图像 (MRI) 的大型数据库，我们将开发新方法，使 DBN 能够处理大型、复杂的图像。我们将研究 DBN 能够从大脑图像组中提取哪些类型的相似性，并确定这些相似性是否可能有助于将大脑图像分类为各种类型的类别，从视觉上明显的类别到非图像衍生的类别，例如临床结果。如果成功，这项工作将使神经影像研究人员能够更有效地利用他们的数据，从而极大地受益。