A statistical method for improved 3D surface detection

Smith, S. and Williams, Ian (2015) A statistical method for improved 3D surface detection. IEEE Signal Processing Letters, 22 (8). pp. 1045-1049. ISSN 10709908

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In this letter, we present a new 3D statistical method for surface detection which provides improvements over competitive methods both in terms of noise suppression and detection of complete surfaces. The methods are applied to both synthetically created image volumes, and MRI data. Accuracy against a ground truth is assessed using the quantitative figure of merit performance measure, with the statistical methods outperforming both a 3D implementation of the gradient Canny operator and a 3D optimal steerable filter method. The results also confirm how 3D surface detection methods avoid missing surface information by successfully locating complete boundaries irrespective of the object orientation and plane of image capture. We conclude that the statistical 3D methods are capable of producing more accurate surface maps in textured images, that reflect the 3D boundary information, improving on current 2D and 3D standards. © 1994-2012 IEEE.

Item Type: Article
Identification Number: https://doi.org/10.1109/LSP.2014.2382172
1 August 2015Published
Uncontrolled Keywords: Image edge detection, image segmentation, multidimensional signal processing, Edge detection, Image segmentation, Magnetic resonance imaging, Object detection, Signal processing, Boundary information, Image edge detection, Multidimensional signal processing, Object orientation, Performance measure, Steerable filters, Surface detection, Surface information, Statistical methods
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Hussen Farooq
Date Deposited: 15 Jul 2016 12:58
Last Modified: 22 Mar 2023 12:01
URI: https://www.open-access.bcu.ac.uk/id/eprint/1174

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