Publication
Title
Fast neural network based x-ray tomography of fruit on a conveyor belt
Author
Abstract
Inline computed tomography (CT) based food inspection requires a fast image reconstruction method. Filtered back projection (FBP) meets this requirement, but relies on many high quality X-ray radiographs, which are often not available in a conveyor belt acquisition geometry. On the other hand. iterative reconstruction methods may yield high quality images even with a small number of radiographs, but are orders of magnitude slower. Recently, a neural network FBP (NN-FBP) method was proposed for parallel beam data that proved to be fast and lead to high quality images. (Pelt et al. 2013a) In this work, we present an NN-FBP based CT reconstruction method for inline inspection. Using neural networks, the method computes application specific filters for a Hilbert transform FBP (hFBP) based reconstruction. Results from the proposed neural network based hFBP (NN-hF BP) method on fan beam X-ray radiographs of apples show that, compared to conventional reconstruction methods, NN-hFBP generates images of high quality in a short reconstruction time.
Language
English
Source (journal)
FRUTIC ITALY 2015: 9TH NUT AND VEGETABLE PRODUCTION ENGINEERING SYMPOSIUM
Source (book)
9th Nut and Vegetable Production Engineering Symposium, MAY 19-22, 2015, Milano, ITALY
Publication
Milano : Aidic servizi srl, 2015
ISBN
978-88-95608-35-8
Volume/pages
44(2015), p. 181-186
ISI
000365994700031
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 15.01.2016
Last edited 09.06.2017
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