Automatic Quality Enhancement of Radiographic Images by Fuzzy Logic
Ali Mirshahi1, Hashem Mirzaei Najafi2, Mohammad-R. Akbarzadeh-T3, Maryam Ebrahimi Nik4
1Dr. Ali MIRSHAHI, Department of Clinical Sciences, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad (FUM), Mashhad, Iran.
2Hashem MIRZAEI NAJAFI, Department of Computer Engineering, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
3Prof. Mohammad-R. Akbarzadeh, Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran.
Manuscript received on June 17, 2015. | Revised Manuscript received on June 29, 2015. | Manuscript published on July 05, 2015. | PP: 24-29 | Volume-5 Issue-3, July 2015. | Retrieval Number: C2635075315/2015©BEIESP

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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/

Abstract: Although much progress has been made in X-ray imaging, conventional radiography is still used in many developing countries as well as less developed countries due to its lower cost and availability. These conventional approaches are however significantly influenced by multiple factors such as sensor and environmental noises, age of developer and fixing materials, exposure factors and the experience of the operator. The goal of this study is to apply a novel post processing technique to get digital image advantages with conventional radiographic images. Specifically, we propose a novel fuzzy system to create a standard gray scale level image. As a result, image details are clearer and can be better enhanced by morphological edge operations. This image enhancement can lead to faster and more accurate interpretation by medical professionals. A number of experiments on rats, rabbits, and birds confirm utility of the proposed approach.
Keywords: Computer-Assisted, Fuzzy Logic, Radiography, Image Enhancement.