Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Frangi based multi-scale level sets for retinal vascular segmentation.

Retinal vascular disease has always been the focus of medical attention. However, segmentation of the retinal vessels from fundus images is still an open problem due to intensity inhomogeneity in the image and thickness diversity of the retinal vessels. In this paper, we propose Frangi based multi-scale level sets to segment retinal vessels from fundus images. Vascular structures are first enhanced by the Frangi filter with local optimal scales being obtained at the same time. The enhanced image and local optimal scales are taken considered as inputs of the proposed level set models. Effectiveness of the proposed multi-scale level sets to their scale fixed versions has been evaluated using DRIVE and STARE image repositories. In addition, the proposed level set models have been tested on the DRIVE and STARE images. Experiments show that the proposed models produce segmentation accuracy at the same level with state-of-the-art methods.

Vessel segmentation using multiscale vessel enhancement and a region based level set model.

Vessel segmentation has always been a considerable challenge task due to the presence of varying thickness of the vessels and weak contrasts of medical image intensities. In this paper, an effective method is proposed, which consists of four steps. Firstly, the input images are converted into gray ones with predetermined weightings aiming at increasing image contrast if they are colorful. Secondly, the image intensities are expanded from regions of interest to the whole image domain with a mirroring operation to avoid introducing undesired boundaries by image filtering operations existing in the next step. Thirdly, an improved multi-scale enhancement method inspired by the Frangi filtering is proposed to enhance image contrast between blood vessels and other objects in the image. Finally, an improved level set model is proposed to segment blood vessels from the enhance images and the original gray images. The proposed method has been evaluated on two retinal vessel image repositories, namely, DRIVE and STARE. Experimental results and comparison with 13 existing methods show that the proposed method produces similar segmentation accuracy at the same level with representative methods in the literature. Its effectiveness on segmentation of other type vessels is also discussed at the end of this paper.

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction.

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.