Postdoc: Integrating Soft Segmentation With Intensity-Based Matching for 2D/3D Image Data Registration

博士后：将软分割与基于强度的匹配相结合以进行 2D/3D 图像数据配准

• 批准号: 0104114
• 负责人: John Adler
• 金额: $ 6.6万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-10-01 至 2003-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0104114&HistoricalAwards=false
• 关键词: Postdoc; Integrating; Soft; Segmentation; Intensity

0104114Shahidi, RaminStanford UniversityCISE Postdoctoral Associates in Experimental Computer Science: Integrating Soft Segmentation with Intensity-Based Matching for 2D/3D Image Data Registration'Soft' segmentation is a recent innovation in image processing which attempts to preserve the maximum amount of information possible in an image while classifying various image elements. Soft segmentation produces flexible (e.g., fuzzy or probabilistic) labels for individual pixels in an image as opposed to forcing a decision about each pixel. Soft segmentation is an effective method for noisy images, such as intra-operative fluoroscopic x-ray images, where preservation of information is critical. In recent years, a variety of promising voxel-property or intensity-based matching algorithms have been developed for three-dimensional (3D) medical image registration, but these algorithms are inadequate in the presence of significant noise. Spine images contain rigid elements (vertebrae) within a deformable structure (spine). Registration is performed on a single vertebra, therefore, spine images contain both structured and unstructured noise. Research is proposed to apply soft labels to the segmentation of two-dimensional (2D) images of the spine for 2D/3D image registration. The postdoctoral associate will assist in 1) adapting an existing fiducial-based clinical spinal navigation system to use image-based fine registration using pre-segmented images, 2) developing a semi-automated segmentation of fluoroscopic images growing a bounding-box around the region of interest, 3) developing both fuzzy and probabilistic 'soft' labels for segmenting the fluoroscopic images, and 4) the convolution of soft labels into gradient and mutual information-based intensity-based matching algorithms.

0104114 Shahidi，RaminStanford UniversityCISE实验计算机科学博士后研究员：将软分割与基于强度的匹配相结合用于2D/3D图像数据配准“软”分割是图像处理中的一项最新创新，它试图在对各种图像元素进行分类的同时保留图像中可能的最大信息量。 软分割产生灵活的（例如，模糊的或概率性的）标记，而不是强制对每个像素进行判定。 软分割是一种有效的方法，噪声图像，如术中荧光透视X射线图像，其中信息的保存是至关重要的。 近年来，针对三维医学图像的配准问题，提出了多种基于体素属性或灰度的匹配算法，但这些算法在存在明显噪声的情况下存在不足。 脊柱图像包含可变形结构（脊柱）内的刚性元素（椎骨）。 配准是在单个椎骨上执行的，因此，脊柱图像包含结构化和非结构化噪声。 研究提出将软标签应用于脊柱的二维（2D）图像的分割以用于2D/3D图像配准。 博士后助理将协助1）调整现有的基于基准的临床脊柱导航系统，以使用预分割图像进行基于图像的精细配准，2）开发荧光透视图像的半自动分割，在感兴趣区域周围生长一个边界框，3）开发模糊和概率“软”标签，用于分割荧光透视图像，以及4）将软标记卷积到基于梯度和互信息的基于强度的匹配算法中。