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Hermite kernels for slice interpolation in medical images
Konstantinos K Delibasis1, Aristides I Kechriniotis, Nicholas D Assimakis
1Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, Greece. kdelibasis@yahoo.com
Summary
New Hermite interpolation kernels improve medical image accuracy. Specific kernels outperform existing methods in accuracy and computational efficiency for image slice interpolation.
Area of Science:
- Medical Imaging
- Numerical Analysis
- Signal Processing
Background:
- Univariate Hermite interpolation of the total degree (HTD) is a complex method using signal values and derivative information.
- Accurate interpolation is crucial for medical image analysis and reconstruction.
Purpose of the Study:
- To derive and evaluate interpolation kernels for univariate Hermite interpolation of the total degree (HTD).
- To assess the performance of these kernels in medical image slice interpolation compared to established methods.
Main Methods:
- Derivation of univariate HTD interpolation kernels using 1st and 2nd order discrete signal derivative approximations.
- Comparative analysis of derived Hermite kernels against established interpolation techniques using medical image data.
Main Results:
- Specific derived Hermite kernels demonstrated superior performance in medical image slice interpolation.
- The proposed kernels achieved higher accuracy, measured by root mean square error (RMSE), compared to other methods.
- Performance gains were observed with comparable computational complexity to existing techniques.
Conclusions:
- The derived univariate HTD Hermite kernels offer a promising alternative for medical image interpolation.
- These kernels provide enhanced accuracy in interpolated medical images, particularly for slice interpolation tasks.
