A MEDICAL MULTI-MODALITY IMAGE FUSION OF CT/PET WITH PCA, DWT METHODS

Abstract

This paper gives a view on the fusion of different modality images like PET and CT (Positron Emission Tomography & Computed Tomography) by two domain methods PCA and DWT methods. The spatial domain is PCA method, and another transformation domain method (DWT). In dwt decomposed coefficients of DWT (discrete wavelet transformation) are applied with the IDWT to get fused image information. Before that, choose a detailed part of decomposed coefficients by maximum selection and averaging the approximated part of DWT coefficients. In applying the PCA using eigen values and eigen vector of larger values as principal components and after to reconstruct using addition to these to get the fussed image of two modalities CT & PET. So that adds complimentary features of both anatomic, physiological and metabolic information in one image, provides better visual information in single image of patients in medical field. The analytic parameters like, MSE, PSNR, ENTROPY results are better enough to prove the methods each other.

Keywords

• Mean Squared Error (MSE)
• Principal Component Analysis (PCA)
• Position Emission Tomography (PET)
• Discrete Wavelet Transform (DWT)
• Peak Signal-to-Noise Ratio (PSNR)