access icon free Non-subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non-subsampled shearlet transform (NSST) and simplified pulse-coupled neural network model (S-PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y-component of PET image are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S-PCCN which is motivated by an adaptive-linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.

Inspec keywords: medical image processing; positron emission tomography; image registration; tumours; neural nets; image colour analysis; biomedical MRI; image fusion; cancer; transforms

Other keywords: NSST; weight region standard deviation; HF coefficients; high-frequency subbands; weight local features; adaptive-linking strength coefficient; spatial frequency; perfect MRI-PET image fusion method; simplified pulse-coupled neural network model; LF coefficients; PET brain image fusion; magnetic resonance imaging; local energy; functional information; source registered MRI image; S-PCNN; YIQ independent components; positron emission tomography image fusion; low-frequency subbands; brain function; spatial characteristics; spatial distortion; Y-component decomposition; low spatial resolution; fusion mutual information; oncology applications; brain tissue anatomy; nonsubsampled shearlet transform-based MRI image; YIQ colour space

Subjects: Nuclear medicine, emission tomography; Patient diagnostic methods and instrumentation; Computer vision and image processing techniques; Integral transforms; Nuclear medicine, emission tomography; Function theory, analysis; Neural computing techniques; Integral transforms; Medical magnetic resonance imaging and spectroscopy; Biology and medical computing; Sensor fusion; Biomedical magnetic resonance imaging and spectroscopy

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