Correlation of sectoral choroidal vascularity with angiographic leakage in central serous chorioretinopathy

Purpose: To correlate sectoral choroidal vascularity with angiographic leakage in eyes with central serous chorioretinopathy (CSCR). Methods: This was a retrospective, cross-sectional study including patients with active CSCR. Multimodal imaging including fundus fluorescein angiography (FFA) and optical coherence tomography (OCT) were performed to identify leakage site and obtain choroidal measurements, respectively. An automated algorithm was used to perform shadow compensation, choroidal boundary localization and binarization, three (3-D) dimensional mapping, and early treatment of diabetic retinopathy study (ETDRS) grid based choroidal quantification that is, choroidal thickness (CT) and choroidal vascularity index (CVI). Nested analysis of variance (ANOVA) was performed to compare CT and CVI in different sectors. Results: Thirty-two eyes with active CSCR were analyzed. CT values varied significantly among the sectors (range, 450.27–482.63 µm; p = 0.005) and rings (range, 459.71–480.45 µm; p < 0.001), however, CVI failed to show significant variation among various segments (sectors, rings, and quadrants; range, 0.53–0.54; all p values > 0.05). Among 25 leaking spots in 25 different sectors, 12 (48%) had an increased CT compared to the overall CT whereas only 24% had increased CVI compared to overall CVI. Mean CT and CVI of the sectors with leakage (427.1 ± 81.1 µm; 0.51 ± 0.05) and remaining sectors without leakage (411.3 ± 73.9 µm; 0.53 ± 0.04) were not statistically different ( p = 0.48; p = 0.12, respectively). Conclusion: Though CT varied in different segments and increased CT corresponded to leakage points on FFA in 48% of eyes, CVI changes were more diffusely spread and local changes in CVI were not predictive of leakage location in eyes with active CSCR.

Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

Irradiance Restoration Based Shadow Compensation Approach for High Resolution Multispectral Satellite Remote Sensing Images

Numerous applications are hindered by shadows in high resolution satellite remote sensing images, like image classification, target recognition and change detection. In order to improve remote sensing image utilization, significant importance appears for restoring surface feature information under shadow regions. Problems inevitably occur for current shadow compensation methods in processing high resolution multispectral satellite remote sensing images, such as color distortion of compensated shadow and interference of non-shadow. In this study, to further settle these problems, we analyzed the surface irradiance of both shadow and non-shadow areas based on a satellite sensor imaging mechanism and radiative transfer theory, and finally develop an irradiance restoration based (IRB) shadow compensation approach under the assumption that the shadow area owns the same irradiance to the nearby non-shadow area containing the same type features. To validate the performance of the proposed IRB approach for shadow compensation, we tested numerous images of WorldView-2 and WorldView-3 acquired at different sites and times. We particularly evaluated the shadow compensation performance of the proposed IRB approach by qualitative visual sense comparison and quantitative assessment with two WorldView-3 test images of Tripoli, Libya. The resulting images automatically produced by our IRB method deliver a good visual sense and relatively low relative root mean square error (rRMSE) values. Experimental results show that the proposed IRB shadow compensation approach can not only compensate information of surface features in shadow areas both effectively and automatically, but can also well preserve information of objects in non-shadow regions for high resolution multispectral satellite remote sensing images.

An Automatic Shadow Compensation Method via a New Model Combined Wallis Filter with LCC Model in High Resolution Remote Sensing Images

Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component I also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image.