An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH) morphology and context) and graph-analysis algorithms (e.g., Dijkstra path). The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels

Evaluating the effect of soil moisture, surface temperature, and humidity variations on MODIS-derived NDVI values

The capability to observe soil moisture frequently and over large regions could significantly enhance our ability to monitor vegetation conditions over time and space. The purpose of this project is to evaluate the effects of soil moisture, temperature, and humidity variations on vegetation conditions in the UAE. Visible and near-infrared channels of MODIS instrument on board of aqua satellite were used in this study. The Normalized Difference Vegetation Index (NDVI) was applied to map the extent of vegetation coverage. It was found in this study that the vegetation areas with NDVI values between 0 and 0.2 have significant correlation with average soil moisture, minimum humidity and maximum temperature and the humidity has the maximum effect on these vegetated areas. However, much lower correlation was found with high NDVI areas