Retinal microaneurysm (MA) is the initial symptom of diabetic retinopathy (DR). The automatic detection of MA is helpful to assist doctors in diagnosis and treatment. Previous algorithms focused on the features of the target itself; however, the local structural features of the target and background are also worth exploring. To achieve MA detection, an efficient local structure awareness-based retinal MA detection with the multi-feature combination (LSAMFC) is proposed in this paper. We propose a novel local structure feature called a ring gradient descriptor (RGD) to describe the structural differences between an object and its surrounding area. Then, a combination of RGD with the salience and texture features is used by a Gradient Boosting Decision Tree (GBDT) for candidate classification. We evaluate our algorithm on two public datasets, i.e., the e-ophtha MA dataset and retinopathy online challenge (ROC) dataset. The experimental results show that the performance of the trained model significantly improved after combining traditional features with RGD, and the area under the receiver operating characteristic curve (AUC) values in the test results of the datasets e-ophtha MA and ROC increased from 0.9615 to 0.9751 and from 0.9066 to 0.9409, respectively.

Infrared small target detection (ISTD) plays a crucial role in precision guidance, anti-missile interception, and military early-warning systems. Existing approaches suffer from high false alarm rates and low detection rates when detecting dim and small targets in complex scenes. A robust scheme for automatically detecting infrared small targets is proposed to address this problem. First, a gradient weighting technique with high sensitivity was used for extracting target candidates. Second, a new collection of features based on local convergence index (LCI) filters with a strong representation of dim or arbitrarily shaped targets was extracted for each candidate. Finally, the collective set of features was inputted to a random undersampling boosting classifier (RUSBoost) to discriminate the real targets from false-alarm candidates. Extensive experiments on public datasets NUDT-SIRST and NUAA-SIRST showed that the proposed method achieved competitive performance with state-of-the-art (SOTA) algorithms. It is also important to note that the average processing time was as low as 0.07 s per frame with low time consumption, which is beneficial for practical applications.