Wavelet-based medical image fusion via a non-linear operator
Medical image fusion has been extensively used to aid medical diagnosis by combining images of various modalities such as Computed Tomography (CT) and Magnetic Resonance Image (MRI) into a single output image that contains salient features from both inputs. This paper proposes a novel fusion algorit...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/55994/ http://psasir.upm.edu.my/id/eprint/55994/1/Wavelet-based%20medical%20image%20fusion%20via%20a%20non-linear%20operator.pdf |
| Summary: | Medical image fusion has been extensively used to aid medical diagnosis by combining images of various modalities such as Computed Tomography (CT) and Magnetic Resonance Image (MRI) into a single output image that contains salient features from both inputs. This paper proposes a novel fusion algorithm through the use of a non-linear fusion operator, based on the low sub-band coefficients of the Discrete Wavelet Transform (DWT). Rather than employing the conventional mean rule for approximation sub-bands, a modified approach is taken by the introduction of a non-linear fusion rule that exploits the multimodal nature of the image inputs by prioritizing the stronger coefficients. Performance evaluation of CT-MRI image fusion datasets based on a range of wavelet filter banks shows that the algorithm boasts improved scores of up to 92% as compared to established methods. Overall, the non-linear fusion rule holds strong potential to help improve image fusion applications in medicine and indeed other fields. |
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