Selection of Local Thresholds for Tomogram Segmentation by Projection Distance Minimization

Abstract. Segmentation is an important step to obtain quantitative information from tomographic data sets. To this end, global thresholding is often used in practice. However, it is usually not possible to obtain an accurate segmentation based on a single, global threshold. Instead, local thresholding schemes can be applied that use a varying threshold, depending on local characteristics of the tomogram. Selecting the best local thresholds is not a straightforward task, as local image features often do not provide sufficient information for choosing a proper threshold. Recently, the concept of projection distance was proposed as a new criterion for evaluating the quality of a tomogram segmentation. In this paper, we describe how Projection Distance Minimization (PDM) can be used to select local thresholds, based on the available projection data from which the tomogram was initially computed. By reprojecting the segmented image, a comparison can be made with the measured projection data...

• Type: Conference
• Year: 2008
• Where: DGCI
• Authors: Kees Joost Batenburg, Jan Sijbers
• Keywords: DGCI 2008, Discrete Geometry, Global Thresholding, Local Thresholding, Local Thresholds