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Multimodal image registration facilitates the combination of complementary information from images acquired with different modalities. Most existing methods require computation of the joint histogram of the images, while some perform joint segmentation and registration in alternate iterations. In this work, we introduce a new non-information-theoretical method for pairwise multimodal image registration, in which the error of segmentation – using both images – is considered as the registration cost function.

This paper introduces a robust image mosaicing method. A variational optical flow (OF) method is first proposed to deal with scenes exhibiting strong specular reflections and few texture information. Then, a general form of descriptors invariant to complex illumination variations is given from which a novel descriptor is obtained. Non-linear transformations computed with the OF fields between the images are used to construct the mosaics. Experimental results demonstrate that the proposed method leads to coherent mosaics, even for complex gastroscopic image sequences.

In recent years, data driven methods have been successfully used for analyzing multi-subject functional magnetic resonance imaging (fMRI) datasets. These methods attempt to learn shared spatial activation maps (SM) or voxel time courses (TC) from temporally or spatially concatenated fMRI datasets respectively. Most of the methods proposed so far do not distinguish whether a particular SM/TC is a group level component or only present in a certain subject dataset.

In recent history, dictionary learning (DL) methods have been successfully used for analyzing multi-subject functional magnetic resonance imaging. These algorithms try to learn group-level spatial activation maps (SM) or voxel time courses (TC) from temporally or spatially concatenated fMRI datasets respectively. However, in multi-subject fMRI studies, we are interested in both group-level TCs as well as SMs.

Remote Photoplethysmography (rPPG) is a contactless noninvasive
method for measuring physiological signals such as
the heart rate (HR) using the light reflected from the facial
tissue. Signal decomposition approaches are used to extract
the heart rate signal from the subtle changes in the skin color.
In this paper, we show that a recently proposed signal decomposition
method, namely nonlinear mode decomposition
(NMD), is quite successful in estimating the heart rate signal
from face videos in the presence of subject motion. Experimental

Segmentation of cardiac MRI images plays a key role in clinical diagnosis. In the traditional diagnostic process, clinical experts manually segment left ventricle (LV), right ventricle (RV) and myocardium to obtain guideline for cardiopathy diagnosis. However, manual segmentation is time-consuming and labor-intensive. In this paper, we propose automatic segmentation and cardiopathy classification in cardiac MRI images
based on deep neural networks. First, we perform object detection based on a YOLO-based network to get region of interest