Multi-Modal Medical Image Fusion using Multi-Resolution Discrete Sine Transform

Quick advancement in high innovation and current medical instrumentations, medical imaging has turned into a fundamental part in many applications such as in diagnosis, research and treatment. Images from multimodal imaging devices usually provide complementary and sometime conflicting information. Information from one image may not be adequate to give exact clinical prerequisites to the specialist or doctor. Of-late, Multi-Model medical image fusion playing a challenging role in current research areas. There are many theories and techniques developed to fuse the multimodal images by researchers. In this paper, introducing a new algorithm called as Multi Resolution Discrete Sine Transform which is used for Multi-Model image fusion in medical applications. Performance and evaluation of this algorithm is presented. The main intention of this paper is to apply DST which is easy to understand and demonstrated method to process image fusion techniques. The proposed MDST based image fusion algorithm performance is compared with that of the well-known wavelet based image fusion algorithm. From the results it is observed that the performance of image fusion using MDST is almost similar to that of wavelet based image fusion algorithm. The proposed MDST based image fusion techniques are computationally very simple and it is suitable. The proposed MDST based image fusion algorithms are computationally, exceptionally basic and it is appropriate for real time medical diagnosis applications

Multisensor Concealed Weapon Detection Using the Image Fusion Approach

Detection of concealed weapons is an increasingly important problem for both military and police since global terrorism and crime have grown as threats over the years. This work presents two image fusion algorithms, one at pixel level and another at feature level, for efficient concealed weapon detection application. Both the algorithms presented in this work are based on the double-density dual-tree complex wavelet transform (DDDTCWT). In the pixel level fusion scheme, the fusion of low frequency band coefficients is determined by the local contrast, while the high frequency band fusion rule is developed with consideration of both texture feature of the human visual system (HVS) and local energy basis. In the feature level fusion algorithm, features are exacted using Gaussian Mixture model (GMM) based multiscale segmentation approach and the fusion rules are developed based on region activity measurement. Experiment results demonstrate the robustness and efficiency of the proposed algorithms

A statistical signal processing approach to image fusion for concealed weapon detection

A statistical signal processing approach to multisensor image fusion is presented for concealed weapon detection (CWD). This approach is based on an image formation model in which the sensor images are described as the true scene corrupted by additive non-Gaussian distortion. The expectation-maximization (EM) algorithm is used to estimate the model parameters and the fused image. We demonstrate the efficiency of this approach by applying this method to fusion of visual and non-visual images with emphasis on CWD applications. 1