A New Approach of MRI and CT-Scan Images Fusion using Texture Segmentation and Fuzzy Weighting in Wavelet Transfer

Subject Areas : Computer Engineering and IT

khalil mowlani ¹ , mehdi jafari ² , malihe hashemi ³

1 - Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran
2 - Department of electronic Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran
3 - Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

Received: 2023-12-11 Accepted : 2024-11-19 Published : 2024-12-27

Keywords: Images Fusion, Medical Image Processing, Discrete Wavelet Transform, Texture Segmentation, Fuzzy Weighting.,

Abstract :

CT images provide information about bony structures but cannot support tissue information, whereas MRI images show details about soft tissues. Obtaining the maximum information and key features from the source images, increasing the visual quality and contrast of the fused image, and reducing the computational tasks remain a major challenge for many medical image fusion algorithms. In this article, the integration of medical images is based on two-dimensional discrete wavelet transform (DWT). First, the original images are decomposed by the Db2 discrete wavelet package into two sets of approximate coefficients and partial coefficients. For the matrix of approximate coefficients, the fuzzy weighting technique of the matrix of approximate coefficients of the input images is used, and for partial coefficients, the average method of the matrix of detail coefficients is used. Weighting uses the mask technique obtained by segmenting the texture of the images. This research has been extended to the composition of color medical images, which effectively prevents color distortion and enhances visual quality. The obtained results show that the proposed algorithm not only performs better in edge and contour detection and visual features, but also has improvements in in quantitative parameter values compared to other researches.

References:

[1] Aghamaleki, J.A., Ghorbani, A.: Image fusion using dual tree discrete wavelet transform and weights optimization. Visual Comput. 39(3), 1181-1191 (2023), https://doi.org/10.1007/s00371-021-02396-9.
[2] Jiang, H. et al.: Casting defect region segmentation method based on dual-channel encoding–fusion decoding network. Expert Syst Appl., 123254 (2024), https://doi.org/10.1016/j.eswa.2024.123254
[3] Bayoudh, K., Knani, R., Hamdaoui, F. Mtibaa, A.: A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. Visual Comput. 38(8), 2939-2970 (2022), https://doi.org/10.1007/s00371-021-02166-7
[4] Dinh, P.: Medical image fusion based on enhanced three-layer image decomposition and Chameleon swarm algorithm, Biomedical Signal Processing and Control,
[5] Volume 84, 2023,104740,ISSN 1746 8094, https://doi.org/10.1016/j.bspc.2023.104740
[6] Shang, X. et al.: Holistic Dynamic Frequency Transformer for image fusion and exposure correction. Inf. Fusion 102, 102073 (2024), https://doi.org/10.1016/j.inffus.2023.102073
[7] Sun, T. et al.: Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses. Nano-Micro Lett. 16, 14 (2024), https://doi.org/10.1007/s40820-023-01235-x
[8] Kittusamy, K. , Kumar, L. S. V. S.: Non-Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion. Comput. Syst. Sci. Eng. 44, 1989-2005 (2023), https://doi.org/10.32604/csse.2023.026501
[9] Pan, Y., Lan, T., Xu, C., Zhang, C. ,Feng, Z.: Recent advances via convolutional sparse representation model for pixel-level image fusion. Multimed. Tools Appl., 1-32 (2023), https://doi.org/10.1007/s11042-023-17584-z
[10] Avcı, D., Sert, E., Özyurt, F. ,Avcı, E.: MFIF-DWT-CNN: Multi-focus ımage fusion based on discrete wavelet transform with deep convolutional neural network. Multimed. Tools Appl. 83, 10951-10968 (2024), https://doi.org/10.1007/s11042-023-16074-6
[11] Luo, Y. et al.: Texture classification combining improved local binary pattern and threshold segmentation. Multimed. Tools Appl., 1-18 (2023), https://doi.org/10.1007/s11042-023-14749-8
[12] Palanisami, D., Mohan, N. ,Ganeshkumar, L.: A new approach of multi-modal medical image fusion using intuitionistic fuzzy set. Biomed. Signal Process. Control. 77, 103762 (2022), https://doi.org/10.1016/j.bspc.2022.103762
[13] Li, Q., Wang, W., Chen, G. ,Zhao, D.: Medical image fusion using segment graph filter and sparse representation. Comput. Biol. Med. 131, 104239 (2021), https://doi.org/10.1016/j.compbiomed.2021.104239
[14] Li, X., Zhou, F., Tan, H., Zhang, W. ,Zhao, C.: Multimodal medical image fusion based on joint bilateral filter and local gradient energy. Inf. Sci. 569, 302-325 (2021), https://doi.org/10.1016/j.ins.2021.04.052
[15] Hermessi, H., Mourali, O. ,Zagrouba, E.: Multimodal medical image fusion review: Theoretical background and recent advances. Signal Process. 183, 108036 (2021), https://doi.org/10.1016/j.sigpro.2021.108036
[16] ULLAH, Hikmat, et al. Multimodality medical images fusion based on local-features fuzzy sets and novel sum-modified-Laplacian in non-subsampled shearlet transform domain. Biomedical Signal Processing and Control, 2020, 57. Jg., S. 101724, https://doi.org/10.1016/j.bspc.2019.101724
[17] Manchanda, M. , Sharma, R.: An improved multimodal medical image fusion algorithm based on fuzzy transform. J. Vis. Commun. Image Represent. 51, 76-94 (2018), https://doi.org/10.1016/j.jvcir.2017.12.011
[18] TANG, Lu, et al. Perceptual quality assessment for multimodal medical image fusion. Signal Processing: Image Communication, 2020, 85. Jg., S. 115852, https://doi.org/10.1016/j.image.2020.115852
[19] TAN, Wei, et al. Multimodal medical image fusion algorithm in the era of big data. Neural Computing and Applications, 2020, S. 1-21, https://doi.org/10.1007/s00521-020-05173-2
[20] XU, Lina, et al. medical image fusion using a modified shark smell optimization algorithm and hybrid wavelet-homomorphic filter. Biomedical Signal Processing and Control, 2020, 59. Jg., S. 101885, https://doi.org/10.1016/j.bspc.2020.101885
[21] Brindha, V., Jayashree, P.: Fusion of radiological images of Glioblastoma Multiforme using weighted average and maximum selection method in 11th International Conference on Advanced Computing (ICoAC), 328-332 (IEEE), https://doi.org/10.1109/ICoAC48765.2019.246861
[22] Bavirisetti, D. P., Kollu, V., Gang, X. ,Dhuli, R.: Fusion of MRI and CT images using guided image filter and image statistics. Int. J. Imaging Syst. Technol. 27, 227-237 (2017), https://doi.org/10.1002/ima.22228
[23] Haribabu, M. , Guruvaiah, V.: Statistical measurements of multi modal MRI–PET medical image fusion using 2D–HT in HSV color space. Procedia Comput. Sci. 165, 209-215 (2019), https://doi.org/10.1016/j.procs.2020.01.090
[24] Nan Jiang, Bin Sheng; Ping Li; Tong-Yee Lee, “PhotoHelper: Portrait Photographing Guidance Via Deep Feature Retrieval and Fusion”, IEEE Trans. Multim. 25, 2226-2238 (2023), https://doi.org/10.1109/TMM.2022.3144890
[25] Liang, N., Medical image fusion with deep neural networks. Scientific Reports, 2024. 14(1): p. 7972, https://doi.org/10.1038/s41598-024-58665-9
[26] Arora, S. , Kaur, A.: Modified edge detection technique using fuzzy inference system. Int. J. Comput. Appl. 44, 9-1 (2012), https://doi.org/10.5120/6409-8757
[27] Anindyaguna, K., Basjaruddin, N. C. ,Saefudin, D.: Overtaking assistant system (OAS) with fuzzy logic method using camera sensorin 2016 2nd International Conference of Industrial, Mechanical, Electrical, and Chemical Engineering (ICIMECE). 89-94 (IEEE), https://doi.org/10.1109/ICIMECE.2016.7910420
[28] Zhang, Z. , Blum, R. S.: A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proceedings of the IEEE 87, 1315-1326 (1999), http://doi.org/10.1109/5.775414
[29] Ming, L. , Shunjun, W.: A novel hybrid image fusion method based on integer lifting wavelet and discrete cosine transformer for visual sensor networks in Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003. 154-159 (IEEE), https://doi.org/10.1007/s11042-018-6676-z
[30] Laws, K. I. in Image processing for missile guidance. 376-381 (SPIE), https://doi.org/10.1080/713820676
[31] Kassner A, Thornhill RE. Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol. 2010 May;31(5):809-16, https://doi.org/10.3174/ajnr.A2061
[32] Targhi, A. T., Hayman, E., Eklundh, J.-O. ,Shahshahani, M. in Asian Conference on Computer Vision. 70-79 (Springer).