RESEARCH ARTICLE


Evaluation of a Mathematical Model for Digital Image Enhancement



Hassem Geha 1, Ibrahim Nasseh 2, *, Marcel Noujeim 3
1 The University of Texas Health Science Center, San Antonio, United States
2 Department of Oral and Maxillofacial Radiology, Lebanese University, Beirut, Lebanon
3 The University of Texas Health Science Center, San Antonio, United States


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Creative Commons License
© Geha et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Oral and Maxillofacial Radiology, Lebanese University, Beirut, Lebanon; Tel/Fax: +9611446413; E-mail: ibrahim.nasseh@gmail.com


Abstract

Objective : The purpose of this study is to compare the detected number of holes on a stepwedge on images resulting from the application of the 5th degree polynomial model compared to the images resulting from the application of linear enhancement. Material and Methods : A 10-step aluminum step wedge with holes randomly drilled on each step was exposed with three different kVp and five exposure times per kVp on a Schick33® sensor. The images were enhanced by brightness/contrast adjustment, histogram equalization and with the 5th degree polynomial model and compared to the original non-enhanced images by six observers in two separate readings. Results : There was no significant difference between the readers and between the first and second reading. There was a significant three-factor interaction among Method, Exposure time, and kVp in detecting holes. The overall pattern was: “Poly” results in the highest counts, “Original” in the lowest counts, with “B/C” and “Equalized” intermediate. Conclusion : The 5th degree polynomial model showed more holes when compared to the other modalities.

Keywords: Contrast, digital imaging, histogram, image enhancement.