A new algorithm for removing salt and pepper noise from color medical images

被引:5
作者
Chanu, Thiyam Romita [1 ]
Singh, Th. Rupachandra [1 ]
Singh, Kh. Manglem [2 ]
机构
[1] Manipur Univ, Dept Comp Sci, Imphal, Manipur, India
[2] NIT Manipur, Dept Comp Sci & Engn, Imphal, Manipur, India
关键词
Impulse noise; Color medical image; Vector median; Salt and pepper noise; VECTOR MEDIAN FILTER; IMPULSE NOISE; QUATERNION;
D O I
10.1007/s11042-023-14378-1
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A new method for eliminating salt and pepper noise from color medical images is formulated in this work. The presence of noise in the medical images degrades image quality, affecting disease analysis, detection, and diagnosis by the doctors. Therefore, removal of noise from the medical image is crucial. For color image, vector median filter is preferred for decreasing presence of salt and pepper noise as it preserves the correlation between the channels. However, applying filter on the image without detecting the noise not only reduces noise, but also produces blurring effect in the homogeneous regions and removes the important features such as textures, edges, thin lines, curves, corners etc. presence in the images. This paper proposes a switching vector median filter that detects the salt and pepper noise in the images prior to the filtering operation to avoid such undesirable effects. The vector median filter is applied in the filtering kernel if the central vector pixel does not lie in the set of healthy vector pixels and the minimum average sums of the distances of the vector pixels that forms the edges in the four directions is more than a predetermined threshold. In comparison to existing common filters, the simulation results demonstrate the proposed filter's superior performance for color medical image in decreasing salt and pepper noise and maintaining details.
引用
收藏
页码:24991 / 25013
页数:23
相关论文
共 42 条
[1]   BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis [J].
Aksac, Alper ;
Demetrick, Douglas J. ;
Ozyer, Tansel ;
Alhajj, Reda .
BMC RESEARCH NOTES, 2019, 12 (1)
[2]   Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method [J].
Aldhyani, Theyazn H. H. ;
Nair, Rajit ;
Alzain, Elham ;
Alkahtani, Hasan ;
Koundal, Deepika .
DIAGNOSTICS, 2022, 12 (10)
[3]   Filtering impulse noise in medical images using information sets [J].
Arora, Shaveta ;
Hanmandlu, Madasu ;
Gupta, Gaurav .
PATTERN RECOGNITION LETTERS, 2020, 139 :1-9
[4]   VECTOR MEDIAN FILTERS [J].
ASTOLA, J ;
HAAVISTO, P ;
NEUVO, Y .
PROCEEDINGS OF THE IEEE, 1990, 78 (04) :678-689
[5]   Color Random Valued Impulse Noise Removal Based on Quaternion Convolutional Attention Denoising Network [J].
Cao, Yiqin ;
Fu, Yangyi ;
Zhu, Zhiliang ;
Rao, Zhechu .
IEEE SIGNAL PROCESSING LETTERS, 2022, 29 :369-373
[6]  
Celebi M.E., 2013, Color Medical Image Analysis
[7]   A two-stage switching vector median filter based on quaternion for removing impulse noise in color images [J].
Chanu, P. Roji ;
Singh, Kh. Manglem .
MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (11) :15375-15401
[8]   Impulse Noise Removal from Medical Images by Two Stage Quaternion Vector Median Filter [J].
Chanu, P. Roji ;
Singh, Kh. Manglem .
JOURNAL OF MEDICAL SYSTEMS, 2018, 42 (10)
[9]   Skin Lesion Segmentation Using Recurrent Attentional Convolutional Networks [J].
Chen, Peng ;
Huang, Sa ;
Yue, Qing .
IEEE ACCESS, 2022, 10 :94007-94018
[10]  
Codella NCF, 2018, I S BIOMED IMAGING, P168, DOI 10.1109/ISBI.2018.8363547