Efficient Utilization of Image Fusion and Interpolation for Medical Image Diagnosis applications

Abstract

— This paper presents a framework for medical image diagnosis of brain tumors. This framework comprises image fusion, image interpolation and image segmentation. The objective of the fusion process is to integerate information from MR and CT images in a single image for better representation of tumors. The fusion is implemented with one of the Dual tree complex wavelet transform (DT-CWT), Discrete wavelet transform (DWT) and principal component analysis (PCA) algorithms to investigate the best one for the application of interest. Interpolation is implemented with one of both polynomial and inverse interpolation techniques. Inverse techniques including linear minimum mean square error (LMMSE) and regularized interpolation are preferred to polynomial technique. After that, threshold segmentation is implemented to isolate the tumor region. Different evolution metrics are used such as accuracy, sensitivity , precision , specifity ,…….. are used to assess the proposed framework. Simulation results prove that the frameworking depending on DWT fusion gives the best results over the existing published techniques