The multimodal medical image fusion is an important application in many medical applications. This is used for the retrieval of complementary information from medical images. The MRI and CT image provides high resolution images with structural and anatomical information. The CT image is used in tumour and anatomical detection and MRI is used to obtain information among tissues. In this paper, w...

Medical images play an important role in medical diagnosis. However, acquiring a large number of datasets with annotations is still difficult task the field. For this reason, research field image-to-image translation combined computer-aided diagnosis, and data augmentation methods based on generative adversarial networks are applied to images. In paper, we try perform unimodal data. The designe...

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy s...

In this paper, we develop data driven registration algorithms, relying on pixel similarity metrics, that enable an accurate (subpixel) rigid registration of dissimilar single or multimodal 2D/3D images. Gross dissimilarities are handled by considering similarity measures related to robust M-estimators. In particular, a novel (robust) similarity metric is proposed for comparing multimodal images...

We tackle here the problem of multimodal image nonrigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neura...

The registration of multimodal images remains an intricate issue, especially when the multimodal image pair shows non overlapping structures, missing data, noise or outliers. In this paper, we present a deformable model-based technique for the rigid registration of 2 0 and 3D multimodal images. The deformable model embeds a priori knowledge of the spatial correspondence and statistical variabil...

We present two new clustering algorithms for medical image segmentation based on the multimodal image registration and the information bottleneck method. In these algorithms, the histogram bins of two registered multimodal 3D-images are clustered by minimizing the loss of mutual information between them. Thus, the clustering of histogram bins is driven by the preservation of the shared informat...

Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medi...

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