The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices

NSCT Based Multimodal Fusion Technique for Medical Images

In this paper my idea is to propose a new approach for multimodal medical image fusion based on NSCT which further gone ease the work for medical images .Multimodality in medical imaging are X-ray, computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), and positron emission tomography (PET). The reason behind writing this paper is to let researcher get acquainted with idea of multimodal image using a technique called as non-sampled contourlet transform (NSCT) by the help of this technique we can capture all relevant information required for medical diagnosis non-sub sampled contourlet transform (NSCT). The two multimodality medical images are first transformed by NSCT into low- and high-frequency components followed by combining the low- and high-frequency components. Phase congruency and directive contrast are main methods which are proposed for various need of low frequency and high frequency coefficients .Finally NSCT Based method is used for medical multimodal images

A Review on Multimodal Medical Image Fusion

Algorithms and devices of multimodal medical image fusion have shown notable achievements in raising the clinical accuracy of decisions based on medical images. In this paper procedure for multi modal medical image fusion and applications of medical imaging modalities have been described .The fusion of medical images has established to be helpful for the treatment of various diseases

Fusion of Images and Videos using Multi-scale Transforms

This thesis deals with methods for fusion of images as well as videos using multi-scale transforms. First, a novel image fusion algorithm based primarily on an improved multi-scale coefficient decomposition framework is proposed. The proposed framework uses a combination of non-subsampled contourlet and wavelet transforms for the initial multi-scale decompositions. The decomposed multi-scale coefficients are then fused twice using various local activity measures. Experimental results show that the proposed approach performs better or on par with the existing state-of-the art image fusion algorithms in terms of quantitative and qualitative performance. In addition, the proposed image fusion algorithm can produce high quality fused images even with a computationally inexpensive two-scale decomposition. Finally, we extend the proposed framework to formulate a novel video fusion algorithm for camouflaged target detection from infrared and visible sensor inputs. The proposed framework consists of a novel target identification method based on conventional thresholding techniques proposed by Otsu and Kapur et al. These thresholding techniques are further extended to formulate novel region-based fusion rules using local statistical measures. The proposed video fusion algorithm, when used in target highlighting mode, can further enhance the hidden target, making it much easier to localize the hidden camouflaged target. Experimental results show that the proposed video fusion algorithm performs much better than its counterparts in terms of quantitative and qualitative results as well as in terms of time complexity. The relative low complexity of the proposed video fusion algorithm makes it an ideal candidate for real-time video surveillance applications