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RISS 인기검색어
- 검색키워드
- 저자 : Sema Koç (검색결과 2 건)
- 무료
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- 유료
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Paraoxonase Activity and Oxidative Status in Patients with Tinnitus
Sema Koç,Servet Akyüz,Battal Tahsin Somuk,Harun Soyalic,Beyhan Yılmaz,Abdullah Taskin,Hasan Bilinc,Nurten Aksoy 대한청각학회 2016 Journal of Audiology & Otology Vol.20 No.1
Background and Objectives: The aim of this study was to investigate serum paraoxanase-1 (PON) activity, total oxidant status (TOS), total antioxidant status (TAS), and the oxidative stress index (OSI) in tinnitus; and to compare the results with data from healthy subjects. Subjects and Methods: A total of 114 subjects-54 patients with tinnitus and 60 healthy controls were enrolled in this study. Serum PON activity, TOS, TAS, and OSI levels were measured. Results: In the tinnitus group, TAS, and PON were significantly lower than in the control group (p<0.001). However, the TOS, and OSI levels were significantly higher in the tinnitus group than in the control group (p<0.001). Conclusions: According to the data obtained from the present study, patients with tinnitus were exposed to potent oxidative stress. Oxidative stress may be the key contributing factor to the pathogenesis of tinnitus.
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Image Restoration by Lifting-Based Wavelet Domain E-Median Filter
Sema Koç,Ergun Er?lebi 한국전자통신연구원 2006 ETRI Journal Vol.28 No.1
In this paper, we propose a method of applying a liftingbased wavelet domain e-median filter (LBWDEMF) for image restoration. LBWDEMF helps in reducing the number of computations. An e-median filter is a type of modified median filter that processes each pixel of the output of a standard median filter in a binary manner, keeping the output of the median filter unchanged or replacing it with the original pixel value. Binary decisionmaking is controlled by comparing the absolute difference of the median filter output and the original image to a preset threshold. In addition, the advantage of LBWDEMF is that probabilities of encountering root images are spread over sub-band images, and therefore the e-median filter is unlikely to encounter root images at an early stage of iterations and generates a better result as iteration increases. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters, then applies an e-median filter in the wavelet domain, transforms the result into the spatial domain, and finally goes through one spatial domain e-median filter to produce the final restored image. Moreover, in order to validate the effectiveness of the proposed method we compare the result obtained using the proposed method to those using a spatial domain median filter (SDMF), spatial domain e-median filter (SDEMF), and wavelet thresholding method. Experimental results show that the proposed method is superior to SDMF, SDEMF, and wavelet thresholding in terms of image restoration.
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