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CT Image Denoising Using Locally Adaptive Shrinkage Rule in Tetrolet Domain

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Kumar, Manoj (Author)
مؤلفين آخرين: Diwakar, Manoj (Co-Author)
المجلد/العدد: مج30, ع1
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2018
الصفحات: 41 - 50
DOI: 10.33948/0584-030-001-005
ISSN: 1319-1578
رقم MD: 974302
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
التصوير المقطعي | الأشعة السينية
كلمات المؤلف المفتاحية:
Image Denoising | Wavelet Transform | Tetrolet Transform | Shrinkage Rule
رابط المحتوى:
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المستخلص: In Computed Tomography (CT), image degradation such as noise and detail blurring is one of the universal problems due to hardware restrictions. The problem of noise in CT images can be solved by image denoising. The main aim of image denoising is to reduce the noise as well as preserve the important features such as edges, corners, textures and sharp structures. Due to the large capability of noise suppression in noisy signals according to neighborhood pixels or coeffi- cients, this paper presents a new technique to denoise CT images with edge preservation in tetrolet domain (Haar-type wavelet transform) where a locally adaptive shrinkage rule is performed on high frequency tetrolet coefficients in such a way that noise can be reduced more effectively. The exper- imental results of the proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with some standard existing methods where it is observed that performance of the proposed scheme is superior to the existing methods in terms of visual quality, MSE, PSNR and Image Quality Index (IQI).
ISSN: 1319-1578

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