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Hypertension during pregnancy, which is essentially a microvascular disease that destroys the end-organ microcirculation, should not be underestimated, as it could lead to organ failure in the kidneys, lungs, and brain. Preassessment of the microcirculatory state through systematic observation of the fundus has been proven to be noninvasive and feasible. Although hypertension in preeclampsia patients will resolve after childbirth, the sticking point is determining the best termination moment. Early diagnosis and treatment can prevent long-term ocular complications and cardiovascular risks for pregnant women in the future. In order to adjust the treatment strategy through more sensitive and precise fundus changes, we comprehensively summarized the common structural changes in the fundus in preeclampsia patients, including changes in the blood vessels, choroid, and retina, as well as the application of quantitative observation for chorioretinal alterations in recent years.

Abstract:The extraction of a valuable set of features and the design of a discriminative classifier are crucial for target recognition in SAR image. Although various features and classifiers have been proposed over the years, target recognition under extended operating conditions (EOCs) is still a challenging problem, e.g., target with configuration variation, different capture orientations, and articulation. To address these problems, this paper presents a new strategy for target recognition. We first propose a low-dimensional representation model via incorporating multi-manifold regularization term into the low-rank matrix factorization framework. Two rules, pairwise similarity and local linearity, are employed for constructing multiple manifold regularization. By alternately optimizing the matrix factorization and manifold selection, the feature representation model can not only acquire the optimal low-rank approximation of original samples, but also capture the intrinsic manifold structure information. Then, to take full advantage of the local structure property of features and further improve the discriminative ability, local sparse representation is proposed for classification. Finally, extensive experiments on moving and stationary target acquisition and recognition (MSTAR) database demonstrate the effectiveness of the proposed strategy, including target recognition under EOCs, as well as the capability of small training size.