Abstract
Pulse-coupled neural networks are a subpart of deep learning (DL) methodologies, which has vast number of applications. One of the preferred applications is multimodal medical image fusion, where different modality images such as MR scan images, CT scan images, and PET scan images are needed to be fused in one of the combinations to provide promising results to diagnose the abnormalities that were unable to detect in the individual images. When accomplishment a precise diagnosis or anatomy of the body, one type of medical imaging modality may not be adequate, imposing the fusion of images from several medical imaging modalities in order to deliver a final result in the immense popular of medical applications. In this work, MRI and PET scan images are fused by means of PCNN and shearlet transformation to yield better results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Singh S, Anand RS (2020) Multimodal medical image sensor fusion model using sparse K-SVD dictionary learning in nonsubsampled shearlet domain. IEEE Trans Instrum Meas 69:593–607
James A, Dasarathy BV (2014) Medical image fusion: a survey of the state of the art. Information Fusion 19:4–19
Garzelli A (2002) Possibilities and limitations of the use of wavelets in image fusion. In: IEEE geoscience and remote sensing symposium, vol 1, pp 66–68
Rashid E, Ansari MD, Gunjan VK, Ahmed M (2020) Improvement in extended object tracking with the vision-based algorithm. In: Gunjan V, Zurada J, Raman B, Gangadharan G (eds) Modern approaches in machine learning and cognitive science: a walkthrough. Studies in computational intelligence, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-38445-6_18
Nageswara Reddy P, Satyanarayana C, Mohan Rao CPVNJ (2022) Brain tumour segmentation using hybrid approach. Machine learning and internet of things for societal issues. Springer, Singapore, pp 117–125
Rashid E, Ansari MD, Gunjan VK, Khan M (2020) Enhancement in teaching quality methodology by predicting attendance using machine learning technique. Modern approaches in machine learning and cognitive science: a walkthrough. Springer, Berlin, pp 227–235
Bhatnagar G, Wu QM, Liu Z (2013) Directive contrast based multimodal medical image fusion in NSCT domain. IEEE Trans Multimedia 15(05):1014–1024
Li S, Kang X, Hu J (2013) Image fusion with guided filtering. Transactions on Image Processing 22(7):2864–2875
Ahuja NJ, Singh N, Kumar A (2018) Development of knowledge capsules for custom-tailored dissemination of knowledge of seismic data interpretation. In: Perez G, Mishra K, Tiwari S, Trivedi M (eds) Networking communication and data knowledge engineering. Lecture notes on data engineering and communications technologies, vol 3. Springer, Singapore. https://doi.org/10.1007/978-981-10-4585-1_16
Shreyamsha Kumar BK (2012) Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform. Springer-Verlag London Limited, London
Malhan Y, Saxena S, Mala S, Shankar A (2022) Geospatial modelling and trend analysis of coronavirus outbreaks using sentiment analysis and intelligent algorithms. In: Garg L, Basterrech S, Banerjee C, Sharma TK (eds) Artificial intelligence in healthcare. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-16-6265-2_1
Venkatanarayan A, Mohan I, Hasan M, Singh N, Chhabra G (2017) Threshold based active queue management (TBAQM) for alleviating DoS/flooding attacks. Journal of Engineering and Applied Sciences (Asian Research Publishing Network)
Du J, Lin W, Xiao B, Nawaz Q (2016) Union Laplacian pyramid with multiple features for medical image fusion. Neuro Computing 194:326–339
Zonga J, Qiua T (2017) Medical image fusion based on sparse representation of classified image patches. Biomed Signal Process Control 34:195–205
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Reddy, Y.P.K., Vaishnavi, A., Devi, M.S., Prasad, M.S., Reddy, B.S. (2023). Multimodal Medical Image Fusion Approach Using PCNN Model and Shearlet Transforms via Max Flat FIR Filter. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2021. Lecture Notes in Electrical Engineering, vol 947. Springer, Singapore. https://doi.org/10.1007/978-981-19-5936-3_73
Download citation
DOI: https://doi.org/10.1007/978-981-19-5936-3_73
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5935-6
Online ISBN: 978-981-19-5936-3
eBook Packages: Computer ScienceComputer Science (R0)