Dual Channel Pulse Coupled Neural Network Algorithm for Fusion of Multimodality Brain Images with Quality Analysis

Abstract

Background: In the review of medical imaging techniques, an important fact that emerged is that radiologists and physicians still are in a need of high-resolution medical images with complementary information from different modalities to ensure efficient analysis. This requirement should have been sorted out using fusion techniques with the fused image being used in image-guided surgery, image-guided radiotherapy and non-invasive diagnosis. Aim: This paper focuses on Dual Channel Pulse Coupled Neural Network (PCNN) Algorithm for fusion of multimodality brain images and the fused image is further analyzed using subjective (human perception) and objective (statistical) measures for the quality analysis. Material and Methods: The modalities used in fusion are CT, MRI with subtypes T1/T2/PD/GAD, PET and SPECT, since the information from each modality is complementary to one another. The objective measures selected for evaluation of fused image were: Information Entropy (IE) - image quality, Mutual Information (MI) – deviation in fused to the source images and Signal to Noise Ratio (SNR) – noise level, for analysis. Eight sets of brain images with different modalities (T2 with T1, T2 with CT, PD with T2, PD with GAD, T2 with GAD, T2 with SPECT-Tc, T2 with SPECT-Ti, T2 with PET) are chosen for experimental purpose and the proposed technique is compared with existing fusion methods such as the Average method, the Contrast pyramid, the Shift Invariant Discrete Wavelet Transform (SIDWT) with Harr and the Morphological pyramid, using the selected measures to ascertain relative performance. Results: The IE value and SNR value of the fused image derived from dual channel PCNN is higher than other fusion methods, shows that the quality is better with less noise. Conclusion: The fused image resulting from the proposed method retains the contrast, shape and texture as in source images without false information or information loss.