Advances in the diagnosis of prostate cancer based on image fusion_Journal of Biomedical Engineering

Authors：

LUO Wenbin , WANG Pei , ZHANG Yiwei ,  SHI Gengqiang

The School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;

Corresponding author：

Keywords：

Image fusion; Prostate cancer; Traditional machine learning; Deep learning

DOI：

10.7507/1001-5515.202403054

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.

Citation： LUO Wenbin, WANG Pei, ZHANG Yiwei, SHI Gengqiang. Advances in the diagnosis of prostate cancer based on image fusion. Journal of Biomedical Engineering, 2024, 41(5): 1078-1084. doi: 10.7507/1001-5515.202403054 Copy

1.	张帆, 黄海锋, 张胜捷, 等. 前列腺穿刺活检的临床应用进展. 现代泌尿外科杂志, 2023, 28(3): 261-264.
2.	陈舰, 武鹏, 胡伟, 等. 基于多参数磁共振和PSMA PET/CT的前列腺靶向穿刺研究进展. 现代肿瘤医学, 2024, 32(2): 387-391.
3.	冯筠, 邓佳慧, 周末, 等. 多模态医学图像配准算法综述. 华中科技大学学报(自然科学版), 2024, 52(5): 29-49.
4.	Venkatesan B, Ragupathy U S, Natarajan I. A review on multimodal medical image fusion towards future research. Multimed Tools Appl, 2022, 82(5): 7361-7382.
5.	王龙. 面向前列腺穿刺的3D MRI/TRUS图像分割与配准. 大连: 大连理工大学, 2022.
6.	Masaya I, Ichiro Y, Masahiro T, et al. Superior detection of significant prostate cancer by transperineal prostate biopsy using MRI-transrectal ultrasound fusion image guidance over cognitive registration. Int J Clin Oncol, 2023, 28(11): 1545-1553.
7.	Nam J K, Song W H, Lee S S, et al. Impact of ultrasonographic findings on cancer detection rate during MRI/US fusion-targeted prostate biopsy. World J Mens Health, 2023, 41(3): 743-749.
8.	Claros O R, Muttin F, Tourinho B R, et al. MP13-19 comparison of caner detection rates in micro-ultrasound biopsies versus robotic. J Urol, 2019, 201(Supplement 4): e184-e185.
9.	Adeel M A, Bahadar K K, Sana S, et al. A review on multimodal medical image fusion: compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. CBM, 2022, 144: 105253.
10.	Sajeer M, Mishra A. A robust and secured fusion based hybrid medical image watermarking approach using RDWT-DWT-MSVD with Hyperchaotic system Fibonacci Q Matrix encryption. Multimed Tools Appl, 2023, 82(24): 37479-37501.
11.	Syed M, Krishna G V P R. Medical image fusion based on multi-scale decomposition using hybrid deep learning network model. Comp M Bio Bio E-IV, 2023, 11(5): 2070-2080.
12.	张鲁, 田春伟, 宋焕生, 等. 多级双树复小波网络用于低剂量CT图像去噪. 计算机工程, 2024, 14(3): 1-11.
13.	Kumar D S, Jainy S, Kamal C A, et al. A novel hybrid multimodal medical image fusion scheme based on non-subsampled shearlet transform. CSSP, 2024, 43(6): 3627-3648.
14.	Jamwal A, Jain S. Empirical curvelet-ridgelet wavelet transform for multimodal fusion of brain images. Curr Med Imaging, 2024, 20(1): 1573-1591.
15.	Mohan P, Sood A, Sharma L, et al. PC-SWT: A hybrid image fusion algorithm of stationary wavelet transformation and principal component analysis. IJEAT, 2020, 9(5): 700-705.
16.	黄渝萍, 李伟生. 医学图像融合方法综述. 中国图象图形学报, 2023, 28(1): 118-143.
17.	Hussain S K, Jaser T A, Tariq A, et al. COVID-19 infection analysis framework using novel boosted CNNs and radiological images. Sci Rep, 2023, 13(1): 21837-21853.
18.	Paralic M, Zelenak K, Kamencay P, et al. Automatic approach for brain aneurysm detection using convolutional neural networks. Appl Sci, 2023, 13(24): 13313-13332.
19.	Jin Qi, Tan Sanqing, Zhang Gui, et al. Visible and infrared image fusion of forest fire scenes based on generative adversarial networks with multi-classification and multi-level constraints. Forests, 2023, 14(10): 1952-1968.
20.	Chen Qihao, Pang Mengqing, Liu Xiuguo, et al. A polarization-spectrum fusion framework based on multiscale transform and generative adversarial network for improving water and different vegetation distinguishability. Int J Appl Earth Obs, 2023, 123(9): 103468-103482.
21.	Nabil C, Tawfik T, Zohra F B. Enhanced independent component analysis and fuzzy C-mean clustering based on novel bat algorithm for noisy image segmentation. Eng Res Express, 2023, 5(4): 2631-2649.
22.	Qian Yao, Liu Gang, Tang Haojie, et al. BTSFusion: fusion of infrared and visible image via a mechanism of balancing texture and salience. Opt Laser Eng, 2024, 173(2): 107925-107943.
23.	Maruturi H, Velmathi G. An improved multimodal medical image fusion approach using intuitionistic fuzzy set and intuitionistic fuzzy cross-correlation. Diagnostics, 2023, 13(14): 2330-2352.
24.	Manjit K, Dilbag S. Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks. J Amb Intel Hum Comp, 2020, 12(8): 2483-2493.
25.	Wang Yan, Qian Hongyang, Shao Xiaoguang, et al. Multimodal convolutional neural networks based on the Raman spectra of serum and clinical features for the early diagnosis of prostate cancer. Spectrochim Acta A, 2023, 293: 122426.
26.	Shatabdi B, Sunita S, Dilbag S. A systematic literature review on multimodal medical image fusion. Multimed Tools Appl, 2023, 83(6): 15845-15913.
27.	Meng Changhan, Huang Mengxing, Li Yunchun, et al. Multi-modal MRI image fusion of the brain based on joint bilateral filter and non-subsampled shearlet transform. J Bio-inspir Com, 2023, 21(1): 26-35.
28.	Padmavathi K, Asha C, Maya K V. A novel medical image fusion by combining TV-L1 decomposed textures based on adaptive weighting scheme. JESTECH, 2020, 23(1): 225-239.
29.	Hammad M, Chelloug A S, Alayed W, et al. Optimizing multimodal scene recognition through mutual information-based feature selection in deep learning models. Appl Sci, 2023, 13(21): 11829-11847.
30.	Ehar N, Shrivastava M. Pixel-level feature extraction model for breast cancer detection. CMC, 2022, 74(2): 3371-3389.
31.	Subbiah V P, Sivakumar P. Multi-modality medical image fusion using hybridization of binary crow search optimization. HCMS, 2019, 23(4): 1-9.
32.	Maqsood S, Javed U. Multi-modal medical image fusion based on two-scale image decomposition and sparse representation. Biomed Signal Proces, 2020, 57(3): 101810-101817.
33.	Goyal S, Singh V, Rani A, et al. FPRSGF denoised non-subsampled shearlet transform-based image fusion using sparse representation. Signal Image Video P, 2020, 14(6): 719-726.
34.	Duan Junwei, Mao Shuqi, Jin Junwei, et al. A novel GA-based optimized approach for regional multimodal medical image fusion with superpixel segmentation. Access, 2021, 9(3): 96353-96366.
35.	Fu Qiang, Ma Ninglan, Liu Xuanwei, et al. Analysis of multispectral polarization imaging image information based on micro-polarizer array. PloS, 2024, 19(1): e0296397.

1. 张帆, 黄海锋, 张胜捷, 等. 前列腺穿刺活检的临床应用进展. 现代泌尿外科杂志, 2023, 28(3): 261-264.
2. 陈舰, 武鹏, 胡伟, 等. 基于多参数磁共振和PSMA PET/CT的前列腺靶向穿刺研究进展. 现代肿瘤医学, 2024, 32(2): 387-391.
3. 冯筠, 邓佳慧, 周末, 等. 多模态医学图像配准算法综述. 华中科技大学学报(自然科学版), 2024, 52(5): 29-49.
4. Venkatesan B, Ragupathy U S, Natarajan I. A review on multimodal medical image fusion towards future research. Multimed Tools Appl, 2022, 82(5): 7361-7382.
5. 王龙. 面向前列腺穿刺的3D MRI/TRUS图像分割与配准. 大连: 大连理工大学, 2022.
6. Masaya I, Ichiro Y, Masahiro T, et al. Superior detection of significant prostate cancer by transperineal prostate biopsy using MRI-transrectal ultrasound fusion image guidance over cognitive registration. Int J Clin Oncol, 2023, 28(11): 1545-1553.
7. Nam J K, Song W H, Lee S S, et al. Impact of ultrasonographic findings on cancer detection rate during MRI/US fusion-targeted prostate biopsy. World J Mens Health, 2023, 41(3): 743-749.
8. Claros O R, Muttin F, Tourinho B R, et al. MP13-19 comparison of caner detection rates in micro-ultrasound biopsies versus robotic. J Urol, 2019, 201(Supplement 4): e184-e185.
9. Adeel M A, Bahadar K K, Sana S, et al. A review on multimodal medical image fusion: compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. CBM, 2022, 144: 105253.
10. Sajeer M, Mishra A. A robust and secured fusion based hybrid medical image watermarking approach using RDWT-DWT-MSVD with Hyperchaotic system Fibonacci Q Matrix encryption. Multimed Tools Appl, 2023, 82(24): 37479-37501.
11. Syed M, Krishna G V P R. Medical image fusion based on multi-scale decomposition using hybrid deep learning network model. Comp M Bio Bio E-IV, 2023, 11(5): 2070-2080.
12. 张鲁, 田春伟, 宋焕生, 等. 多级双树复小波网络用于低剂量CT图像去噪. 计算机工程, 2024, 14(3): 1-11.
13. Kumar D S, Jainy S, Kamal C A, et al. A novel hybrid multimodal medical image fusion scheme based on non-subsampled shearlet transform. CSSP, 2024, 43(6): 3627-3648.
14. Jamwal A, Jain S. Empirical curvelet-ridgelet wavelet transform for multimodal fusion of brain images. Curr Med Imaging, 2024, 20(1): 1573-1591.
15. Mohan P, Sood A, Sharma L, et al. PC-SWT: A hybrid image fusion algorithm of stationary wavelet transformation and principal component analysis. IJEAT, 2020, 9(5): 700-705.
16. 黄渝萍, 李伟生. 医学图像融合方法综述. 中国图象图形学报, 2023, 28(1): 118-143.
17. Hussain S K, Jaser T A, Tariq A, et al. COVID-19 infection analysis framework using novel boosted CNNs and radiological images. Sci Rep, 2023, 13(1): 21837-21853.
18. Paralic M, Zelenak K, Kamencay P, et al. Automatic approach for brain aneurysm detection using convolutional neural networks. Appl Sci, 2023, 13(24): 13313-13332.
19. Jin Qi, Tan Sanqing, Zhang Gui, et al. Visible and infrared image fusion of forest fire scenes based on generative adversarial networks with multi-classification and multi-level constraints. Forests, 2023, 14(10): 1952-1968.
20. Chen Qihao, Pang Mengqing, Liu Xiuguo, et al. A polarization-spectrum fusion framework based on multiscale transform and generative adversarial network for improving water and different vegetation distinguishability. Int J Appl Earth Obs, 2023, 123(9): 103468-103482.
21. Nabil C, Tawfik T, Zohra F B. Enhanced independent component analysis and fuzzy C-mean clustering based on novel bat algorithm for noisy image segmentation. Eng Res Express, 2023, 5(4): 2631-2649.
22. Qian Yao, Liu Gang, Tang Haojie, et al. BTSFusion: fusion of infrared and visible image via a mechanism of balancing texture and salience. Opt Laser Eng, 2024, 173(2): 107925-107943.
23. Maruturi H, Velmathi G. An improved multimodal medical image fusion approach using intuitionistic fuzzy set and intuitionistic fuzzy cross-correlation. Diagnostics, 2023, 13(14): 2330-2352.
24. Manjit K, Dilbag S. Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks. J Amb Intel Hum Comp, 2020, 12(8): 2483-2493.
25. Wang Yan, Qian Hongyang, Shao Xiaoguang, et al. Multimodal convolutional neural networks based on the Raman spectra of serum and clinical features for the early diagnosis of prostate cancer. Spectrochim Acta A, 2023, 293: 122426.
26. Shatabdi B, Sunita S, Dilbag S. A systematic literature review on multimodal medical image fusion. Multimed Tools Appl, 2023, 83(6): 15845-15913.
27. Meng Changhan, Huang Mengxing, Li Yunchun, et al. Multi-modal MRI image fusion of the brain based on joint bilateral filter and non-subsampled shearlet transform. J Bio-inspir Com, 2023, 21(1): 26-35.
28. Padmavathi K, Asha C, Maya K V. A novel medical image fusion by combining TV-L1 decomposed textures based on adaptive weighting scheme. JESTECH, 2020, 23(1): 225-239.
29. Hammad M, Chelloug A S, Alayed W, et al. Optimizing multimodal scene recognition through mutual information-based feature selection in deep learning models. Appl Sci, 2023, 13(21): 11829-11847.
30. Ehar N, Shrivastava M. Pixel-level feature extraction model for breast cancer detection. CMC, 2022, 74(2): 3371-3389.
31. Subbiah V P, Sivakumar P. Multi-modality medical image fusion using hybridization of binary crow search optimization. HCMS, 2019, 23(4): 1-9.
32. Maqsood S, Javed U. Multi-modal medical image fusion based on two-scale image decomposition and sparse representation. Biomed Signal Proces, 2020, 57(3): 101810-101817.
33. Goyal S, Singh V, Rani A, et al. FPRSGF denoised non-subsampled shearlet transform-based image fusion using sparse representation. Signal Image Video P, 2020, 14(6): 719-726.
34. Duan Junwei, Mao Shuqi, Jin Junwei, et al. A novel GA-based optimized approach for regional multimodal medical image fusion with superpixel segmentation. Access, 2021, 9(3): 96353-96366.
35. Fu Qiang, Ma Ninglan, Liu Xuanwei, et al. Analysis of multispectral polarization imaging image information based on micro-polarizer array. PloS, 2024, 19(1): e0296397.

Previous Article
Research progress of breast pathology image diagnosis based on deep learning
Next Article
Research and implementation of intelligent diagnostic system for temporomandibular joint disorder

Journal of Biomedical Engineering

Advances in the diagnosis of prostate cancer based on image fusion

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content