Paper
22 March 2001 Biologically based sensor fusion for medical imaging
Mario Aguilar, Aaron L. Garrett
Author Affiliations +
Abstract
We present an architecture for the fusion of multiple medical image modalities that enhances the original imagery and combines the complimentary information of the various modalities. The design principles follow the organization of the color vision system in humans and primates. Mainly, the design of within- modality enhancement and between-modality combination for fusion is based on the neural connectivity of retina and visual cortex. The architecture is based on a system developed for night vision applications while the first author was at MIT Lincoln Laboratory. Results of fusing various modalities are presented, including: a) fusion of T1-weighted and T2-weighted MRI images, b) fusion of PD, T1 weighted, and T2-weighted, and c) fusion of SPECT and MRI/CT. The results will demonstrate the ability to fuse such disparate imaging modalities with regard to information content and complimentarities. These results will show how both brightness and color contrast are used in the resulting color fused images to convey information to the user. In addition, we will demonstrate the ability to preserve the high spatial resolution of modalities such as MRI even when combined with poor resolution images such as from SPECT scans. We conclude by motivating the use of the fusion method to derive more powerful image features to be used in segmentation and pattern recognition.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mario Aguilar and Aaron L. Garrett "Biologically based sensor fusion for medical imaging", Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); https://doi.org/10.1117/12.421102
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CITATIONS
Cited by 19 scholarly publications.
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KEYWORDS
Image fusion

Magnetic resonance imaging

Single photon emission computed tomography

Computed tomography

Image enhancement

Medical imaging

Neural networks

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