Automated Multimodal image fusion for brain tumor detection

Kemal Polat

1 Chitkara University Institute of Engineering and Technology, Chitkara University, India. Email: [email protected]

2 Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India. Email: [email protected]

3 Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India. Email: [email protected]

4 Department of Computer Science, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India. Email: [email protected]

5 Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey. Email: [email protected]

*Corresponding Author: [email protected]

In medical domain, various multimodalities such as Computer tomography (CT) and Magnetic resonance imaging (MRI) are integrated into a resultant fused image. Image fusion (IF) is a method by which vital information can be preserved by extracting all important information from the multiple images into the resultant fused image. The analytical and visual image quality can be enhanced by the integration of different images. In this paper, a new algorithm has been proposed on the basis of guided filter with new fusion rule for the fusion of different imaging modalities such as MRI and Fluorodeoxyglucose images of brain for the detection of tumor. The performance of the proposed method has been evaluated and compared with state-of-the-art image fusion techniques using various qualitative as well as quantitative evaluation metrics. From the results, it has been observed that more information has achieved on edges and content visibility is also high as compared to the other techniques which makes it more suitable for real applications. The experimental results are evaluated on the basis of with-reference and without-references metric such as standard deviation, entropy, peak signal to noise ratio, mutual information etc.

Medical, Brain, Magnetic resonance imaging, Fluorodeoxyglucose, Principal Component Analysis, Multi resolution singular value decomposition

Harpreet Kaur, Deepika Koundal, Virendar Kadyan, Navneet Kaur, and Kemal Polat (2021). Automated Multimodal image fusion for brain tumor detection. Journal of Artificial Intelligence and Systems, 3, 68–82. https://doi.org/10.33969/AIS.2021.31005.

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