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

Stanford University, Stanford CA

Investigators

Abstract

0104114 Shahidi, Ramin Stanford University CISE 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.