Title: Medical image fusion using PCNN and Poisson-hidden Markov model

Authors: Biswajit Biswas; Biplab Kanti Sen

Addresses: Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, 700098, India ' Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, 700098, India

Abstract: The combination of multiple images into a single image without loss of any generosity, is known as image fusion. In medical diagnosis, the magnetic resonance imaging (MRI) gives the brain tissue anatomy without any functional information whereas the Positron emission tomography (PET) image gives the brain function with low spatial resolution. Combination of anatomical and functional tomographic images is required for better diagnosis purpose. Image fusion technique combines the spatial resolution of the functional images by merging them with a high-resolution anatomic image. This paper proposes a novel medical image fusion technique based on pulse coupled neural net (PCNN) and Poisson hidden Markov model (PHMM) that satisfies fusion criterion. The excellency of the proposed approach is verified by the comparison with some of the state-of-the-art techniques in terms of several quantitative fusion evaluation indexes.

Keywords: medical image fusion; PET-MRI; shearlet; PCNN; entropy.

DOI: 10.1504/IJSISE.2018.091883

International Journal of Signal and Imaging Systems Engineering, 2018 Vol.11 No.2, pp.73 - 84

Received: 25 Mar 2017
Accepted: 07 Nov 2017
Published online: 20 May 2018 *

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