This paper presents an original method for three-dimensional elastic registration of multimodal images. We propose to make use of a scheme that iterates between correcting for intensity differences between images and performing standard monomodal registration. The core of our contribution resides in providing a method that finds the transformation that maps the intensities of one image to those...

Many medical image analysis problems that involve multimodal images lend themselves to solutions that involve class posterior density function images. This paper presents a method for large deformation exemplar class posterior density template estimation. This method generates a representative anatomical template from an arbitrary number of topologically similar multi-modal image sets using lar...

Abstract—Image reconstruction is an active research field, due to the increasing need for geometric 3D models in movie industry, games, virtual environments and in medical fields. 3D image reconstruction aims to arrive at the 3D model of an object, from its 2D images taken at different viewing angles. Medical images are multimodal, which includes MRI, CT scan image, PET and SPECT images. Of the...

Analysis of multispectral or multitemporal images requires proper geometric alignment of the images to compare corresponding regions in each image volume. Retrospective three-dimensional alignment or registration of multimodal medical images based on features intrinsic to the image data itself is complicated by their different photometric properties, by the complexity of the anatomical objects ...

In this paper, multimodal learning for facial expression recognition (FER) is proposed. The multimodal learning method makes the first attempt to learn the joint representation by considering the texture and landmark modality of facial images, which are complementary with each other. In order to learn the representation of each modality and the correlation and interaction between different moda...

In this paper, a new wavelet representation for multimodal images is presented. The idea for this representation is based on the first fundamental form that provides a local measure for the contrast of a multimodal image. In this paper, this concept is extended towards multiscale fundamental forms using the dyadic wavelet transform of Mallat. The multiscale fundamental forms provide a local mea...

Background: Glioma is the most common primary brain tumor, and early detection of tumors is important in the treatment planning for the patient. The precise segmentation of the tumor and intratumoral areas on the MRI by a radiologist is the first step in the diagnosis, which, in addition to the consuming time, can also receive different diagnoses from different physicians. The aim of this study...

This paper discusses the final conclusions and empirically derived guidelines for the investigation of the role of some multimodal metaphors in On-Line Assessment: An Audio-Visual Approach interfaces using avatar, images, visual text, and earcons in terms of efficiency, effectiveness and user satisfaction as well as learning performance. This experimental approach was evaluated using two differ...

The baseline copus of a new multimodal database, acquired in the framework of the FP6 EU BioSec Integrated Project, is presented. The corpus consist of fingerprint images acquired with three different sensors, frontal face images from a webcam, iris images from an iris sensor, and voice utterances acquired both with a close-talk headset and a distant webcam microphone. The BioSec baseline corpu...

Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it not always possible acquire a complete set MRIs, and the problem missing modalities causes severe performance degradation existing methods. In this work, we present first attempt exploit Transformer for that robust any combinatorial subse...