Research progress of details enhancement methods in medical images_Journal of Biomedical Engineering

Authors：

WANG Yu , JIN Zhenyi ,  WANG Yuanjun

School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China;

Corresponding author：

Keywords：

medical image; details; enhancement; quantitative evaluation

DOI：

10.7507/1001-5515.201705055

Video：

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Abstract Full text Figures/Tables Video References Cited by

Effective medical image enhancement method can not only highlight the interested target and region, but also suppress the background and noise, thus improving the quality of the image and reducing the noise while keeping the original geometric structure, which contributes to easier diagnosis in disease based on the image enhanced. This article carries out research on strengthening methods of subtle structure in medical image nowadays, including images sharpening enhancement, rough sets and fuzzy sets, multi-scale geometrical analysis and differential operator. Finally, some commonly used quantitative evaluation criteria of image detail enhancement are given, and further research directions of fine structure enhancement of medical images are discussed.

Citation： WANG Yu, JIN Zhenyi, WANG Yuanjun. Research progress of details enhancement methods in medical images. Journal of Biomedical Engineering, 2018, 35(4): 651-655. doi: 10.7507/1001-5515.201705055 Copy

1.	Agarwal J, Bedi S S. Implementation of hybrid image fusion technique for feature enhancement in medical diagnosis. Human-centric Computing and Information Sciences, 2015, 5(1): 1-17.
2.	Li Bo, Xie Wei. Adaptive fractional differential approach and its application to medical image enhancement. Comput Electr Eng, 2015, 45: 324-335.
3.	Liu Lu, Jia Zhenhong, Yang Jie, et al. A medical image enhancement method using adaptive thresholding in NSCT domain combined unsharp masking. Int J Imaging Syst Technol, 2015, 25(3): 199-205.
4.	Phophalia A, Mitra S K. Rough set based bilateral filter design for denoising brain MR images. Appl Soft Comput, 2015, 33: 1-14.
5.	Chaira T. An improved medical image enhancement scheme using type Ⅱ fuzzy set. Appl Soft Comput, 2014, 25: 293-308.
6.	Yang Jinzhu, Ma Shuang, Sun Qi, et al. Improved Hessian multiscale enhancement filter. Biomed Mater Eng, 2014, 24(6): 3267-3275.
7.	Moreno R, Smedby Ö. Gradient-based enhancement of tubular structures in medical images. Med Image Anal, 2015, 26(1): 19-29.
8.	Jin Zhenyi, Wang Yuanjun, Chen Zengai, et al. Medical image fusion in gradient domain with structure tensor. Journal of Medical Imaging and Health Informatics, 2016, 6(5): 1314-1318.
9.	杨娜, 冯运, 魏颖. 分数阶微分的婴幼儿脑 MR 图像增强算法. 中国图象图形学报, 2016, 21(12): 1696-1706.
10.	Inbarani H H, Bagyamathi M, Azar A T. A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl, 2015, 26(8): 1859-1880.
11.	Pal S K, King R A. Image enhancement using smoothing with fuzzy sets. IEEE Trans Syst Man Cybern, 1981, 11(7): 494-501.
12.	Deng W, Deng H, Cheng L. Enhancement of brain tumor MR images based on intuitionistic fuzzy sets[C]// International Symposium on Multispectral Image Processing and Pattern Recognition. Enshi: International Society for Optics and Photonics, 2015: 98140H.
13.	Adams B, Dehaan P, Tabor R, et al. A novel approach of medical image enhancement based on wavelet transform. Conserv Genet Resour, 2013, 5(2): 573-575.
14.	Liu Shuqin. Study on medical image enhancement based on wavelet transform fusion algorithm. Journal of Medical Imaging and Health Informatics, 2017, 7(2): 388-392.
15.	Hai N T. Wavelet-based image fusion for enhancement of ROI in CT Image. JBEMi, 2014, 1(4): 1-13.
16.	Dong Limin, Yang Qingxiang, Wu Haiyong, et al. High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform. Neurocomputing, 2015, 159: 268-274.
17.	Yang Yong, Tong Song, Huang Shuying, et al. Log-Gabor energy based multimodal medical image fusion in NSCT domain. Comput Math Methods Med, 2014, (2): 835481.
18.	Wang Jingjing, Jia Zhenhong, Qin Xizhong, et al. Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast. Int J Imaging Syst Technol, 2015, 25(1): 7-14.
19.	Li Qiang, Sone S, DoiK. Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys, 2003, 30(8): 2040-2051.
20.	Chen K, Yin Q, Jia X, et al. Image enhancement based improved multi-scale Hessian matrix for coronary angiography. IJCA, 2015, 126(10): 1-4.
21.	Yang S F, Cheng C H. Fast computation of Hessian-based enhancement filters for medical images. Comput Methods Programs Biomed, 2014, 116(3): 215-225.
22.	Liu Xingbin, Mei Wenbo, Du Huiqian. Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion. Neurocomputing, 2017, 235: 131-139.
23.	王大凯, 侯榆青, 彭进业. 图像处理的偏微分方程方法. 北京: 科学出版社, 2008.
24.	Vicas C, Nedevschi S. Detecting curvilinear features using structure tensors. IEEE Trans Image Process, 2015, 24(11): 3874-3887.
25.	步蕊蕊, 汪葛, 王远军, 等. 基于三维结构张量的 CT 肺血管树增强. 中国医学物理学杂志, 2016, 33(4): 336-341.
26.	Singh S, Bovis K. An evaluation of contrast enhancement techniques for mammographic breast masses. IEEE Trans Inf Technol Biomed, 2005, 9(1): 109-119.
27.	Han Shoudong, Xu Wei, Tao Wenbing, et al. Color-texture cosegmentation based on nonlinear compact multi-scale structure tensor and TV-flow. Signal Processing, 2017, 131: 456-471.
28.	Zhao Wenda, Xu Zhijun, Zhao Jian. Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion. Information Fusion, 2016, 27(C): 138-149.
29.	Zeng Dong, Gao Yuanyuan, Huang Jing, et al. Penalized weighted least-squares approach for multienergy computed tomography image reconstruction via structure tensor total variation regularization. Comput Med Imaging Graph, 2016, 53: 19-29.

1. Agarwal J, Bedi S S. Implementation of hybrid image fusion technique for feature enhancement in medical diagnosis. Human-centric Computing and Information Sciences, 2015, 5(1): 1-17.
2. Li Bo, Xie Wei. Adaptive fractional differential approach and its application to medical image enhancement. Comput Electr Eng, 2015, 45: 324-335.
3. Liu Lu, Jia Zhenhong, Yang Jie, et al. A medical image enhancement method using adaptive thresholding in NSCT domain combined unsharp masking. Int J Imaging Syst Technol, 2015, 25(3): 199-205.
4. Phophalia A, Mitra S K. Rough set based bilateral filter design for denoising brain MR images. Appl Soft Comput, 2015, 33: 1-14.
5. Chaira T. An improved medical image enhancement scheme using type Ⅱ fuzzy set. Appl Soft Comput, 2014, 25: 293-308.
6. Yang Jinzhu, Ma Shuang, Sun Qi, et al. Improved Hessian multiscale enhancement filter. Biomed Mater Eng, 2014, 24(6): 3267-3275.
7. Moreno R, Smedby Ö. Gradient-based enhancement of tubular structures in medical images. Med Image Anal, 2015, 26(1): 19-29.
8. Jin Zhenyi, Wang Yuanjun, Chen Zengai, et al. Medical image fusion in gradient domain with structure tensor. Journal of Medical Imaging and Health Informatics, 2016, 6(5): 1314-1318.
9. 杨娜, 冯运, 魏颖. 分数阶微分的婴幼儿脑 MR 图像增强算法. 中国图象图形学报, 2016, 21(12): 1696-1706.
10. Inbarani H H, Bagyamathi M, Azar A T. A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl, 2015, 26(8): 1859-1880.
11. Pal S K, King R A. Image enhancement using smoothing with fuzzy sets. IEEE Trans Syst Man Cybern, 1981, 11(7): 494-501.
12. Deng W, Deng H, Cheng L. Enhancement of brain tumor MR images based on intuitionistic fuzzy sets[C]// International Symposium on Multispectral Image Processing and Pattern Recognition. Enshi: International Society for Optics and Photonics, 2015: 98140H.
13. Adams B, Dehaan P, Tabor R, et al. A novel approach of medical image enhancement based on wavelet transform. Conserv Genet Resour, 2013, 5(2): 573-575.
14. Liu Shuqin. Study on medical image enhancement based on wavelet transform fusion algorithm. Journal of Medical Imaging and Health Informatics, 2017, 7(2): 388-392.
15. Hai N T. Wavelet-based image fusion for enhancement of ROI in CT Image. JBEMi, 2014, 1(4): 1-13.
16. Dong Limin, Yang Qingxiang, Wu Haiyong, et al. High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform. Neurocomputing, 2015, 159: 268-274.
17. Yang Yong, Tong Song, Huang Shuying, et al. Log-Gabor energy based multimodal medical image fusion in NSCT domain. Comput Math Methods Med, 2014, (2): 835481.
18. Wang Jingjing, Jia Zhenhong, Qin Xizhong, et al. Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast. Int J Imaging Syst Technol, 2015, 25(1): 7-14.
19. Li Qiang, Sone S, DoiK. Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys, 2003, 30(8): 2040-2051.
20. Chen K, Yin Q, Jia X, et al. Image enhancement based improved multi-scale Hessian matrix for coronary angiography. IJCA, 2015, 126(10): 1-4.
21. Yang S F, Cheng C H. Fast computation of Hessian-based enhancement filters for medical images. Comput Methods Programs Biomed, 2014, 116(3): 215-225.
22. Liu Xingbin, Mei Wenbo, Du Huiqian. Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion. Neurocomputing, 2017, 235: 131-139.
23. 王大凯, 侯榆青, 彭进业. 图像处理的偏微分方程方法. 北京: 科学出版社, 2008.
24. Vicas C, Nedevschi S. Detecting curvilinear features using structure tensors. IEEE Trans Image Process, 2015, 24(11): 3874-3887.
25. 步蕊蕊, 汪葛, 王远军, 等. 基于三维结构张量的 CT 肺血管树增强. 中国医学物理学杂志, 2016, 33(4): 336-341.
26. Singh S, Bovis K. An evaluation of contrast enhancement techniques for mammographic breast masses. IEEE Trans Inf Technol Biomed, 2005, 9(1): 109-119.
27. Han Shoudong, Xu Wei, Tao Wenbing, et al. Color-texture cosegmentation based on nonlinear compact multi-scale structure tensor and TV-flow. Signal Processing, 2017, 131: 456-471.
28. Zhao Wenda, Xu Zhijun, Zhao Jian. Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion. Information Fusion, 2016, 27(C): 138-149.
29. Zeng Dong, Gao Yuanyuan, Huang Jing, et al. Penalized weighted least-squares approach for multienergy computed tomography image reconstruction via structure tensor total variation regularization. Comput Med Imaging Graph, 2016, 53: 19-29.

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