3D Adaptive SMCM Filtering of CT Images

被引：0

作者：

Draganov, Ivo ^{[1
]}

Mironov, Rumen ^{[1
]}

机构：

[1] Tech Univ Sofia, 8 Kliment Ohridski Blvd, Sofia 1756, Bulgaria

来源：

PROCEEDINGS OF THE THE 11TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS'2021), VOL 1 | 2021年

关键词：

AWGN; SMCM filter; Gaussian filter; CT image; 3D; NOISE;

D O I：

10.1109/IDAACS53288.2021.9660875

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a new implementation of the Saint-Marc-Chen-Medioni (SMCM) filter is proposed in a 3D variant. It uses adaptive selection of the threshold k, found from a 2D projection of the input image. The algorithm has been tested over Computed Tomography (CT) images with Additive White Gaussian Noise (AWGN) at variance levels 0.01 and 0.001. It leads to better quality of filtered images in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), compared to 3D Gaussian and 3D Averaging filters. The proposed 3D SMCM filter is considered applicable for filtering of other types of 3D images, such as Magnetic Resonance Images (MRI).

引用

页码：333 / 336

页数：4

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