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首页> 外文学位 >Interactive 3D GPU-based breast mass lesion segmentation method based on level sets for DCE-MRI images.
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Interactive 3D GPU-based breast mass lesion segmentation method based on level sets for DCE-MRI images.

机译：基于水平集的DCE-MRI图像的基于交互式3D GPU的乳房肿块病变分割方法。

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摘要

A new method for the segmentation of 3D breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images, using parallel programming with general purpose computing on graphics processing units (GPGPUs), is proposed. The method has two main parts: a pre- processing step and a segmentation algorithm. In the pre-processing step, DCE-MRI images are registered using an intensity-based rigid transformation algorithm based on gradient descent. After the registration, voxels that correspond to breast lesions are enhanced using the Na ??ve Bayes machine learning classifier. This classifier is trained to identify four different classes inside breast images: lesion, normal tissue, chest and background. Training is performed by manually selecting 150 voxels for each of the four classes from images in which breast lesions have been confirmed by an expert in the field. Thirteen attributes obtained from the kinetic curves of the selected voxels are later used to train the classifier. Finally, the classifier is used to increase the intensity values of voxels labeled as lesions and to decrease the intensities of all other voxels. The post- processed images are used for volume segmentation of the breast lesions using a level set method based on the active contours without edges (ACWE) algorithm. The segmentation algorithm is implemented in OpenCL for the GPGPUs to accelerate the original model by parallelizing two main steps of the segmentation process: the computation of the signed distance function (SDF) and the evolution of the segmented curve. The proposed framework uses OpenGL to display the segmented volume in real time, allowing the physician to obtain immediate feedback on the current segmentation progress. The proposed implementation of the SDF is compared with an optimal implementation developed in Matlab and achieves speedups of 25 and 12 for 2D and 3D images, respectively. Moreover, the OpenCL implementation of the segmentation algorithm is compared with an optimal implementation of the narrow-band ACWE algorithm. Peak speedups of 55 and 40 are obtained for 2D and 3D images, respectively. The segmentation algorithm has been developed as open source software, with different versions for 2D and 3D images, and can be used in different areas of medical imaging as well as in areas within computer vision, such like tracking, robotics and navigation.
机译：提出了一种在动态对比增强磁共振成像（DCE-MRI）图像中分割3D乳腺病变的新方法，该方法采用并行编程和图形处理单元（GPGPU）上的通用计算。该方法有两个主要部分：预处理步骤和分割算法。在预处理步骤中，使用基于梯度下降的基于强度的刚性变换算法对DCE-MRI图像进行配准。注册后，使用Naveve Bayes机器学习分类器增强与乳房病变相对应的体素。该分类器经过训练可以识别乳房图像内的四个不同类别：病变，正常组织，胸部和背景。训练是通过从本领域的专家已确认乳房病变的图像中为四个类别中的每个类别手动选择150个体素来进行的。从所选体素的动力学曲线获得的13个属性随后用于训练分类器。最后，使用分类器增加标记为病变的体素的强度值，并降低所有其他体素的强度。使用基于无边缘活动轮廓（ACWE）算法的水平集方法，将后处理后的图像用于乳腺病变的体积分割。分割算法是在OpenCL中为GPGPU实现的，可通过并行执行分割过程的两个主要步骤来加速原始模型：有符号距离函数（SDF）的计算和分割曲线的演变。所提出的框架使用OpenGL实时显示分割后的体积，使医生可以立即获得有关当前分割进度的反馈。将SDF的拟议实现与在Matlab中开发的最佳实现进行了比较，分别针对2D和3D图像实现了25和12的加速。此外，将分割算法的OpenCL实现与窄带ACWE算法的最佳实现进行了比较。对于2D和3D图像，分别获得55和40的峰值加速。分割算法已开发为开放源代码软件，具有用于2D和3D图像的不同版本，可用于医学成像的不同领域以及计算机视觉中的领域，例如跟踪，机器人和导航。

著录项

  • 作者

    Zavala-Romero, Olmo Sebastian.;

  • 作者单位

    The Florida State University.;

  • 授予单位 The Florida State University.;
  • 学科 Medical imaging.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 105 p.
  • 总页数 105
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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