Degree Type
Thesis
Date of Award
1997
Degree Name
Master of Science
Department
Electrical and Computer Engineering
Abstract
Industrial investigation of material structure and composition is an integral part of the manufacturing design flow. It is possible to evaluate these properties by both destructive and non-destructive means. Non-destructive evaluation of materials is attractive for obvious reasons and x-ray NDE (Non-Destructive Evaluation) is a well established discipline. X-ray images of materials (represented and stored in the form of radiographs) are capable of providing valuable information regarding the presence of material defects such as, voids, cracks and inclusions. A common medium used to store an x-ray image is the film or radiograph. This is an analog representation of the x-ray image, produced by the photographic effect. This grayscale representation of the material under investigation, when analyzed, is able to provide the necessary information regarding the presence of defects. The human brain has the ability to recognize patterns and differentiate minute variations in the grayscales of the radiograph, so long as these variations are within a particular range. In order to overcome this limitation of the human visual mechanism and to facilitate the objectives of storage, processing and transmission, it is necessary to transform this representation of the x-ray image as a radiograph, into a digital form. This also helps to extract quantitative physical parameters from a digitized image, which is not possible with an analog image which is only good at providing a qualitative overview of an image.
DOI
https://doi.org/10.31274/rtd-180813-6112
Publisher
Digital Repository @ Iowa State University, http://lib.dr.iastate.edu
Copyright Owner
Raghuram Madabushi,
Copyright Date
1997
Language
en
Date Available
2013-12-12
File Format
application/pdf
File Size
69 pages
Recommended Citation
Madabushi, Raghuram, "Photo-densitometry: radiograph digitization and algorithmic enhancement of x-ray images" (1997). Retrospective Theses and Dissertations. 243.
https://lib.dr.iastate.edu/rtd/243