• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Medical Physics >Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation
【2h】

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

机译：非线性降维技术在乳腺MRI分割中的比较分析

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

>Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis.>Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B1 inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions.>Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%).>Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.
机译：>目的：使用放射成像方法可视化解剖结构是医学上区分正常组织与病理组织的重要工具，并且可以生成大量数据供放射科医生阅读。集成这些大数据集既困难又耗时。一种新方法同时使用有监督和无监督的先进机器学习技术来可视化和分割放射线数据。这项研究描述了一种结合了小波变换和非线性降维（NLDR）方法的新型混合方案在乳腺磁共振成像（MRI）数据上的应用，该技术使用了三种完善的NLDR技术，即ISOMAP，局部线性嵌入（ LLE）和扩散图（DfM），以进行比较性能分析。>方法：使用3T扫描仪扫描了25名乳腺病变受试者。使用的MRI序列为T1加权，T2加权，扩散加权成像（DWI）和动态对比度增强（DCE）成像。混合方案包括两个步骤：数据的预处理和后处理。预处理步骤用于B1不均匀性校正，图像配准和基于小波的图像压缩，以对数据进行匹配和去噪。在后处理步骤中，将MRI参数视为数据尺寸，并应用基于NLDR的混合方法将MRI参数整合到称为嵌入式图像的单个图像中。这是通过将所有像素强度从较高维度映射到较低维度（嵌入式）空间来实现的。为了进行验证，作者将混合NLDR与使用合成数据的线性主成分分析（PCA）和多维缩放（MDS）方法进行了比较。对于临床应用，作者使用乳腺MRI数据，使用对比后DCE MRI图像进行比较并评估分割病变的一致性。>结果：基于NLDR的混合方法能够定义和对合成和临床数据进行细分。在综合数据中，作者证明了NLDR方法与常规线性DR方法相比的性能。 NLDR方法可以成功进行结构分割，而在大多数情况下，PCA和MDS会失败。 NLDR方法能够高精度地分割不同的乳房组织类型，并且乳房MRI数据的嵌入图像证明了不同类型的乳房组织（即脂肪，腺体和有病变的组织）之间的模糊边界（> 86％） 。>结论：提出的混合NLDR方法能够高精度地分割临床乳腺数据，并构建嵌入式图像以可视化不同放射学参数的贡献。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号