UCT CS Research Document Archive

Identification and Reconstruction of Bullets from Multiple X-Rays

Perkins, Simon (2004) Identification and Reconstruction of Bullets from Multiple X-Rays. MSc, Department of Computer Science, University of Cape Town.

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The 3D shape and position of objects inside the human body are commonly detected using Computed Tomography (CT) scanning. CT is an expensive diagnostic option in economically disadvantaged areas and the radiation dose experienced by the patient is significant.
In this dissertation, we present a technique for reconstructing the 3D shape and position of bullets from multiple X-rays. This technique makes us of ubiquitous X-ray equipment and a small number of X-rays to reduce the radiation dose. Our work relies on Image Segmentation and Volume Reconstruction techniques.
We present a method for segmenting bullets out of X-rays, based on their signature in intensity profiles. This signature takes the form of a distinct plateau which we model with a number of parameters. This model is used to identify horizontal and vertical line segments within an X-Ray corresponding to a bullet signature. Regions containing confluences of these line segments are selected as bullet candidates. The actual bullet is thresholded out of the region based on a range of intensities occupied by the intensity profiles that contributed to the region.
A simple Volume Reconstruction algorithm is implemented that back-projects the silhouettes of bullets obtained from our segmentation technique. This algorithm operates on a 3D voxel volume represented as an octree. The reconstruction is reduced to the 2D case by reconstructing a slice of the voxel volume at a time.
We achieve good results for our segmentation algorithm. When compared with a manual segmentation, our algorithm matches 90% of the bullet pixels in nine of the twelve test X-rays. Our reconstruction algorithm produces an acceptable results: It achieves a 70% match for a test case where we compare a simulated bullet with a reconstructed bullet.

EPrint Type:Electronic Thesis or Dissertation
Subjects:I Computing Methodologies: I.3 COMPUTER GRAPHICS
ID Code:136
Deposited By:Chetty, Marshini
Deposited On:10 June 2004