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Detecting and quantifying abnormalities in the movement of the heart walls such as hypokinesia, akinesia and dyskinesia and measuring their severity is a critical step in the assessment and treatment of ischemic and non-ischemic heart disease. These so-called contraction abnormalities are generally manifested by a decrease in the amplitude of the cardiac contraction reflecting hypokinesia and a complete absence of wall movement indicating akinesia. In case of dyskinesia, the wall is characterized by an abnormal movement, most often ventricular. In the non-pathological case, when the ventricle contracts in systole, it thickens and tends to approach the center of the cavity while in case of dyskinesia it tends to move away. In medical imaging, several methods for regional assessment of cardiac contractile function have been developed. The aim of this article is to review the most relevant approaches available in magnetic resonance imaging (MRI) such as parametric imaging, cardiac contour segmentation and deep learning. At the end of this study, we compared the previously mentioned approaches after explaining their principles, their advantages and disadvantages. The comparison showed that deep learning represents the most precise method in terms of segmentation and quantification of the contraction anomalies.

International audience; In medical imaging, it has been shown that watermarking can improve data protection and content enrichment. In this paper, an adaptive watermarking algorithm performed in the wavelet domain is proposed which exploits a human visual system (HVS) and a Fuzzy Inference System (FIS). Human visual system (HVS) is adopted to further ensure the watermark invisibility. The FIS is utilized to compute the optimum watermark weighting function that would enable the embedding of the maximum-energy and imperceptible watermark. For the purpose of security and robustness, a watermark sequence is embedded by selectively modifying the middle-frequency parts of the image. The experimental results achieved demonstrate that the proposed algorithm is robust against both, signal processing and geometric attacks.

International audience; Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS) model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image's blocked DCT based transform domain. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of interested watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks. The experimental results show that such method can survive of common image processing operations and has good adaptability for automated watermark embedding.

International audience; Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms. In this work, ACO is introduced to tackle the problem of blind watermark extraction in medical images. This approach is able to establish a pheromone matrix that represents the hidden information presented at each pixel position in, the insertion zone, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the image's intensity values. Experimental results on images are presented to illustrate the merit and feasibility of the proposed method.