Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Velmathi Guruviah
 -- 1458 2023-07-27 09:24:19 |
2 format correct
Catherine Yang
 -4 word(s) 1454 2023-07-27 09:43:48 | |
3 I hope to increasing and progress in this domain.
Nahla Nurelmadina
 + 46 word(s) 1500 2023-07-28 01:02:18 | |
4 format correct
Catherine Yang
 -1 word(s) 1499 2023-07-28 03:04:32 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Haribabu, M.; Guruviah, V.; Nurelmadina, N. An Improved Multimodal Medical Image Fusion Approach. Encyclopedia. Available online: https://encyclopedia.pub/entry/47344 (accessed on 14 December 2024).
Haribabu M, Guruviah V, Nurelmadina N. An Improved Multimodal Medical Image Fusion Approach. Encyclopedia. Available at: https://encyclopedia.pub/entry/47344. Accessed December 14, 2024.
Haribabu, Maruturi, Velmathi Guruviah, Nahla Nurelmadina. "An Improved Multimodal Medical Image Fusion Approach" Encyclopedia, https://encyclopedia.pub/entry/47344 (accessed December 14, 2024).
Haribabu, M., Guruviah, V., & Nurelmadina, N. (2023, July 27). An Improved Multimodal Medical Image Fusion Approach. In Encyclopedia. https://encyclopedia.pub/entry/47344
Haribabu, Maruturi, et al. "An Improved Multimodal Medical Image Fusion Approach." Encyclopedia. Web. 27 July, 2023.
An Improved Multimodal Medical Image Fusion Approach
Edit
This entry is adapted from the peer-reviewed paper 10.3390/diagnostics13142330

Multimodal medical image fusion (MMIF) is the process of merging different modalities of medical images into a single output image (fused image) with a significant quantity of information to improve clinical applicability. It enables a better diagnosis and makes the diagnostic process easier. In medical image fusion (MIF), an intuitionistic fuzzy set (IFS) plays a role in enhancing the quality of the image, which is useful for medical diagnosis. The research introduces an innovative approach to multimodal medical image fusion using intuitionistic fuzzy set theory. This approach shows promising results in improving the quality and accuracy of fused medical images, making it a valuable contribution to the field of medical image processing and diagnosis.

medical imaging image fusion disease diagnosis intuitionistic fuzzy set

1. Introduction

In past decades, image fusion has matured significantly in the application fields such as medical [1], military [2][3], and remote sensing [4]. Image fusion is a prominent application in the medical field for better analysis of human organs and tissues. In general, the medical image data is available from various imaging techniques such as magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), computed tomography (CT), T1-weighted MR, T2-weighted MR, positron emission tomography (PET), and single-photon emission computed tomography (SPECT) [5]. Each technique has different characteristics.
Multimodal medical images are widely characterized into two types: anatomical and functional modalities, respectively. Anatomical modalities are, namely, MRI, MRA, T1-weighted MR, T2-weighted MR, and CT. CT images represent a clear bone structure with lower distortion but do not distinguish physical changes, while MRI images provide delicate tissue information with high spatial resolution. CT imaging is used to diagnose diseases such as muscle disease, vascular conditions, bone fractures and tumors etc. MRI imaging is used to diagnose various issues in medial regions such as brain tumors, multiple sclerosis, lung cancer and treatment, brain hemorrhage, and dementia etc. Magnetic resonance angiography, or MRA, is a subset of MRI that utilizes magnetic fields and radio waves, which create images of the body’s arteries, helping clinicians to detect blood flow abnormalities. The weighted MR-T1 images reveal fat, while weighted MR-T2 images provide water content.
Functional modalities are PET, and SPECT. PET imaging gives functionality of human organs with high sensitivity. The PET imaging technology is used to diagnosis different diseases such as Alzheimer’s disease, Parkinson’s disease, cerebrovascular accident, and hematoma. The other application areas of PET imaging are lung and breast cancer diagnosis, and cancer treatment.
SPECT imaging provides blood flow information with minimal spatial resolution, and is used for different diagnoses, namely, brain and bone disorders, and heart problems. The application areas in SPECT imaging are pelvis irradiation detection and treatment, vulvar cancer, breast cancer assessment, and head and neck cancer diagnosis [6][7]. However, single medical image data cannot provide the required information for diagnosis. To overcome this, multimodal medical image fusion is necessary.
Multimodal medical image fusion is the process of merging different modalities of medical images into a single output image. Its advantages include decreased uncertainty, resilient system performance, and higher reliability, all of which contribute to more accurate diagnosis, thus improving treatment. From the literature, authors have reported various multimodality combinations. Fusion of T1- and T2-weighted MR images produce a fused image, and is used to identify tumor regions [8]. The soft and hard tissue information from MRI and CT images, respectively, are combined into a single resultant image by fusion resulting in better image analysis [9]. The T1-weighted MR and MRA [10] combination provides perfect lesion locations with delicate tissues. The MRI–PET [11] combination and MRI–SPECT [12] combinations provide anatomical and functional information in a single image, which is used to better diagnosis disease and medical-related problems. The objective of this research article is to examine the relevance and advancement of information fusion approaches in medical imaging for investigation of clinical aspects and better treatment.
In any fusion strategy, two important requirements should be satisfied: it should not add any artifacts or blocking effects to the resultant image; and no information should be lost throughout the fusion process.
Image fusion techniques are broadly classified into three levels [13], namely, pixel-level, feature-level, and decision-level. In pixel-level fusion, image pixel values are directly merged. In feature level fusion, various salient features are involved in the fusion process such as texture and shape. In decision-level fusion, the input images are fused based on multiple algorithms with decision rules.

2. An Improved Multimodal Medical Image Fusion Approach Using Intuitionistic Fuzzy Set and Intuitionistic Fuzzy Cross-Correlation

The preeminent research issue in medical image processing is to obtain maximum content of information by combining various modalities of medical images. Various existing techniques are included in this literature such as the simple average (Avg), maximum, and minimum methods. The average method provides a fused image with low contrast, while the maximum and minimum methods provide the less enhanced fused images. The Brovey method [14] gives color distortions. Hybrid fusion methods such as the intensity-hue saturation (IHS) and principal component analysis (PCA) [15] combination provides a degraded fused image with spatial distortions. However, the pyramid decomposition-based method [16] shows better spectral information, but the required edge information is not sufficient. Discrete cosine transform (DCT) [17] and singular value decomposition (SVD) [18] methods give a fused image, which has a more complementary nature but does not show clear boundaries of the tumor region. The multi-resolution techniques, such as discrete wavelet transform (DWT) [19], provides better localization in time and frequency domains, but cannot give the shift-invariance due to down-sampling. To overcome this the redundant wavelet transform (RWT) [20] was employed. However, the above technique is highly complex and cannot provide sufficient edge information. The contourlet transform (CONT) technique [21] provides more edge information in a fused image but does not provide the shift invariance. Shift invariance is the most desirable property and is applied in various applications of image processing. These are: image watermarking [22], image enhancement [23], image fusion [24], and image deblurring [25]. The above mentioned drawbacks are addressed by the non-subsampled contourlet transform (NSCT) [26] and non-subsampled Shearlet transform (NSST) [27][28]. Hybrid combinations of fusion techniques such as DWT and fuzzy logic [29] provide a fused image with low contrast because of the higher uncertainties and vagueness, which is present in a fused image.
In general, medical images have poor illumination which means low contrast and poor visibility in some parts, which indicates uncertainties and vagueness. Visibility and enhancement are the required criteria in the medical field to diagnose the disease accurately. In the literature, various image enhancement techniques are reported, namely, gray-level transformation [30] and histogram-based methods [31]. Yet, these methods are not properly improving the quality of medical images. Zadeh [32] proposed a mathematical approach, namely, a fuzzy set in 1965. This fuzzy set approach has played a significant role by removing the vagueness present in the image. However, it did not eliminate the uncertainties. A fuzzy set does not provide reasonable results regarding more uncertainties because it considers only one uncertainty. This uncertainty is in the form of membership function, that lies between the range 0 to 1, where zero indicates the false membership function, and one indicates the true membership function. In the year 1986 Atanassov [33] proposed a generalized version of the fuzzy set i.e., intuitionistic fuzzy set (IFS), which handles more uncertainties in the form of three degrees. These degrees are membership, non-membership, and hesitation degrees. The IFS technique is highly precise, and flexible in order to handle uncertainties and ambiguity problems.
The research gaps and drawbacks of various medical image fusion techniques are discussed and listed in Table 1:
Table 1. Comparison of the existing fusion methods.
Fusion Methods Modalities Merits Demerits
IHS and PCA MRI-PET Good spatial features and better color visualization in a fused image. Low contrast and distorted boundaries.
Pyramid MRI-CT Preserves better outlines in the fused image. Due to a lack of spatial orientation selectivity, the unwanted edges and blocking effects exist in the fused image.
SVD MRI-CT Provides better quality fused image. Fails to show the clear boundaries of the tumor region.
DWT MRI-CT, MRI-PET Provides good localization in both time and frequency. Has more complexity and lack of edges information.
CONT MRI-CT Fused image has better edges and is superior to DWT and Curvelet transform. Does not provide the shift invariance, may cause blocking effects
NSCT MRI-CT Superior to traditional transform techniques in terms of directionality. Complexity is high.
NSST MRI-CT Fusion process is superior to NSCT with lower complexity. Low brightness and contrast due to uncertainties, and high computational time.
The main contribution of this research article is described as follows:
  • A novel intuitionistic fuzzy set is used for the fusion process, which can enhance the fused image quality and complete the fusion process successfully.
  • The intuitionistic fuzzy images are created by using the optimum value, α, which can be obtained from intuitionistic fuzzy entropy.
  • The Intuitionistic cross-correlation function is employed to measure the correlation between intuitionistic fuzzy images and then produce a fused image without uncertainty and vagueness.
  • The proposed fusion algorithm proves that the fused image has good contrast and enhanced edges and is superior to other existing methods both visually and quantitatively.

References

  1. Azam, M.A.; Khan, K.B.; Salahuddin, S.; Rehman, E.; Khan, S.A.; Khan, M.A.; Kadry, S.; Gandomi, A.H. A Review on Multimodal Medical Image Fusion: Compendious Analysis of Medical Modalities, Multimodal Databases, Fusion Techniques and Quality Metrics. Comput. Biol. Med. 2022, 144, 105253.
  2. Ma, J.; Liu, Y.; Jiang, J.; Wang, Z.; Xu, H.; Huo, X.; Deng, Y.; Shao, K. Infrared and Visible Image Fusion with Significant Target Enhancement. Entropy 2022, 24, 1633.
  3. Deveci, M.; Gokasar, I.; Pamucar, D.; Zaidan, A.A.; Wen, X.; Gupta, B.B. Evaluation of Cooperative Intelligent Transportation System Scenarios for Resilience in Transportation Using Type-2 Neutrosophic Fuzzy VIKOR. Transp. Res. Part A Policy Pract. 2023, 172, 103666.
  4. Mary, S.R.; Pachar, S.; Srivastava, P.K.; Malik, M.; Sharma, A.; Almutiri, G.T.; Atal, Z. Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images. Comput. Intell. Neurosci. 2022, 2022, 2668567.
  5. James, A.P.; Dasarathy, B.V. Medical Image Fusion: A Survey of the State of the Art. Inf. Fusion 2014, 19, 4–19.
  6. Kumar, M.; Kaur, A. Amita Improved Image Fusion of Colored and Grayscale Medical Images Based on Intuitionistic Fuzzy Sets. Fuzzy Inf. Eng. 2018, 10, 295–306.
  7. Venkatesan, B.; Ragupathy, U.S.; Natarajan, I. A Review on Multimodal Medical Image Fusion towards Future Research. Multimed. Tools Appl. 2023, 82, 7361–73824.
  8. Palanisami, D.; Mohan, N.; Ganeshkumar, L. A New Approach of Multi-Modal Medical Image Fusion Using Intuitionistic Fuzzy Set. Biomed. Signal Process. Control 2022, 77, 103762.
  9. Prakash, O.; Park, C.M.; Khare, A.; Jeon, M.; Gwak, J. Multiscale Fusion of Multimodal Medical Images Using Lifting Scheme Based Biorthogonal Wavelet Transform. Optik 2019, 182, 995–1014.
  10. Kumar, P.; Diwakar, M. A Novel Approach for Multimodality Medical Image Fusion over Secure Environment. Trans. Emerg. Telecommun. Technol. 2021, 32, e3985.
  11. Dilmaghani, M.S.; Daneshvar, S.; Dousty, M. A New MRI and PET Image Fusion Algorithm Based on BEMD and IHS Methods. In Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, 2–4 May 2017; pp. 118–121.
  12. Panigrahy, C.; Seal, A.; Mahato, N.K. MRI and SPECT Image Fusion Using a Weighted Parameter Adaptive Dual Channel PCNN. IEEE Signal Process. Lett. 2020, 27, 690–694.
  13. Kaur, H.; Koundal, D.; Kadyan, V. Image Fusion Techniques: A Survey. Arch. Comput. Methods Eng. 2021, 28, 4425–4447.
  14. Wang, Z.; Ziou, D.; Armenakis, C.; Li, D.; Li, Q. A Comparative Analysis of Image Fusion Methods. IEEE Trans. Geosci. Remote Sens. 2005, 43, 1391–1402.
  15. He, C.; Liu, Q.; Li, H.; Wang, H. Multimodal Medical Image Fusion Based on IHS and PCA. Procedia Eng. 2010, 7, 280–285.
  16. Li, M.; Dong, Y. Image Fusion Algorithm Based on Contrast Pyramid and Application. In Proceedings of the 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), Shenyang, China, 20–22 December 2013; pp. 1342–1345.
  17. Tang, J. A Contrast Based Image Fusion Technique in the DCT Domain. Digit. Signal Process. 2004, 14, 218–226.
  18. Liang, J.; He, Y.; Liu, D.; Zeng, X. Image Fusion Using Higher Order Singular Value Decomposition. IEEE Trans. Image Process. 2012, 21, 2898–2909.
  19. Prasad, P.; Subramani, S.; Bhavana, V.; Krishnappa, H.K. Medical Image Fusion Techniques Using Discrete Wavelet Transform. In Proceedings of the 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 27–29 March 2019; pp. 614–618.
  20. Li, X.; He, M.; Roux, M. Multifocus Image Fusion Based on Redundant Wavelet Transform. IET Image Process. 2010, 4, 283.
  21. Khare, A.; Srivastava, R.; Singh, R. Edge Preserving Image Fusion Based on Contourlet Transform. In Proceedings of the Image and Signal Processing: 5th International Conference, ICISP 2012, Agadir, Morocco, 28–30 June 2012; Volume 7340, pp. 93–102.
  22. Sinhal, R.; Sharma, S.; Ansari, I.A.; Bajaj, V. Multipurpose Medical Image Watermarking for Effective Security Solutions. Multimed. Tools Appl. 2022, 81, 14045–14063.
  23. Liu, M.; Mei, S.; Liu, P.; Gasimov, Y.; Cattani, C. A New X-Ray Medical-Image-Enhancement Method Based on Multiscale Shannon–Cosine Wavelet. Entropy 2022, 24, 1754.
  24. Liu, S.; Wang, M.; Yin, L.; Sun, X.; Zhang, Y.-D.; Zhao, J. Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation. Front. Comput. Neurosci. 2022, 15, 133.
  25. Chen, X.; Wan, Y.; Wang, D.; Wang, Y. Image Deblurring Based on an Improved CNN-Transformer Combination Network. Appl. Sci. 2023, 13, 311.
  26. Ganasala, P.; Kumar, V. CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain. J. Digit. Imaging 2014, 27, 407–418.
  27. Qiu, C.; Wang, Y.; Zhang, H.; Xia, S. Image Fusion of CT and MR with Sparse Representation in NSST Domain. Comput. Math. Methods Med. 2017, 2017, 9308745.
  28. Liu, X.; Mei, W.; Du, H. Multi-Modality Medical Image Fusion Based on Image Decomposition Framework and Nonsubsampled Shearlet Transform. Biomed. Signal Process. Control 2018, 40, 343–350.
  29. Sanjay, A.R.; Soundrapandiyan, R.; Karuppiah, M.; Ganapathy, R. CT and MRI Image Fusion Based on Discrete Wavelet Transform and Type-2 Fuzzy Logic. Int. J. Intell. Eng. Syst. 2017, 10, 355–362.
  30. Cao, G.; Huang, L.; Tian, H.; Huang, X.; Wang, Y.; Zhi, R. Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction. Comput. Electr. Eng. 2018, 66, 569–582.
  31. Salem, N.; Malik, H.; Shams, A. Medical Image Enhancement Based on Histogram Algorithms. Procedia Comput. Sci. 2019, 163, 300–311.
  32. Zadeh, L.A. Fuzzy Sets. Inf. Control 1965, 8, 338–353.
  33. Atanassov, K.T. Intuitionistic Fuzzy Sets. Fuzzy Sets Syst. 1986, 20, 87–96.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Engineering, Biomedical
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Maruturi Haribabu
,
Velmathi Guruviah
,
Nahla Nurelmadina
View Times: 485
Revisions: 4 times (View History)
Update Date: 28 Jul 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback