Abstract. Analysing Roman coins found in archaeology sites has been traditionally done manually by an operator using volumetric image slices provided by a computed tomography scanner. In order to automate the counting process, a good segmentation for the coins has to be achieved to separate the touching surfaces of the coins. Separating touching surfaces in volumetric images has not yet attracted much attention. In this paper we propose a new method based on using a form of pressure to separate the intersecting surfaces. We analogise the background of the image to be filled with an ideal gas. The pressure at a point has an inverse relationship with the volume of homogeneous material surrounding it. By studying the pressure space, the locations of intersecting surfaces are highlighted and encouraging segmentation results are achieved. Our analysis concerns a selection of images, naturally demonstrating success, together with an analysis of the new technique's sensitivity to noise. IntroductionComputed tomography imaging is an increasingly popular source for information about three dimensional objects, with many applications ranging from medical to industrial. Scans can contain multiple objects with the same density or single objects containing smaller ones with similar density. The placement of the objects in the 3D space can be random and in some cases the surfaces of those objects touch which makes it difficult to separate them using conventional thresholding and segmentation techniques, motivating development of a higher level process. An example of such a problem is the CT scanned jar ( Fig. 1) which contains a set of Roman coins. This set of data contains coins with similar density randomly placed with different orientations and locations within the jar. The problem associated with this particular set of data relies in the high attenuation factor for the material from which the coins are made which in turn increases the chance of touching surfaces in the volumetric image especially for the coins in the centre of the jar. Separating objects with the same density and texture is challenging due to the absence of techniques for detecting in 3D the regions of intersection between the objects, impeding the possibility of counting the coins.Pressure Based Segmentation in Volumetric Images 239 Fig. 1. CT image of Roman coins inside a jarMany approaches have been developed to solve the problem of separating touching objects in two dimensional (2D) space. The two main application concern separating rice grains [1] and counting cells in microscope images [2]. A traditional approach involves thresholding, corner detection and joining points of interest to create a binary image of disconnected objects. On the other hand, there is no such technique for 3D image analysis. The literature provides some model-based methods that have been used to separate left and right lungs [3]. The search for regions of interest uses images where lungs intersect, to minimize the computational demands. Edge or surface detection can be appli...

We present a new approach to extracting moving spheres from a sequence of 3D point clouds. The new 3D velocity Hough Transform (3DVHT) incorporates motion parameters in addition to structural parameters in an evidence gathering process to accurately detect moving spheres at any given point cloud from the sequence. We demonstrate its capability to detect spheres which are obscured within the sequence of point clouds, which conventional approaches cannot achieve. We apply our algorithm on real and synthetic data and demonstrate the ability of detecting fully occluded spheres by exploiting inter-frame correlation within the 3D point cloud sequence.

Abstract-It has been recently shown that preclinical analysis of computed tomography 3D image volumes can provide essential information to find the optimal position of an implant in hip replacement procedures. In order to extract such data, proper segmentation is crucial. Many of the currently-available methods depend on manually segmented data as the first step. Inherent difficulties concern the similar density of adjacent structures, and that physically-separated structures appear to touch in scanned imagery. In this study, we describe a new technique based on pressure analogy that depends on the local features of the image to accurately and automatically segment and visualize the femur bone and separate it from the acetabulum. The Dice coefficient was employed to study the similarity between the surface area of the segmentations compared with the manually segmented data, and a high value has been achieved. The same method also showed promising results in segmenting other limbs such as the pelvis, tibia and fibula bones.