Medical image fusion by adaptive Gaussian PCNN and improved Roberts operator

Pravesh Vajpayee, Chinmaya Panigrahy, Anil Kumar

Abstract: Medical image fusion analyzes multiple images obtained by the same/different medical modalities and constructs a robust image that is more useful for physicians by merging the complementary details contained in these images. Recently, pulse coupled neural network (PCNN) models constructed efficient image fusion algorithms, but at the expense of many parameters. Here, a novel adaptive Gaussian PCNN (AGPCNN) model is proposed that constitutes few parameters, adopts adaptive linking strength, and employs a Gaussian filter to effectively combine the surrounding neurons. In this paper, a new medical image fusion algorithm is introduced in the non-subsampled Shearlet transform domain that applies the novel AGPCNN to combine the high-pass sub-bands, whereas a new improved Roberts operator-based mechanism is incorporated to merge the low-pass sub-bands. The power of the proposed method is demonstrated using the experimental results of seven latest methods with twelve objective metrics on ten diverse medical image pairs that include the image pairs of an AIDS dementia complex patient.