Information of retinal vasculature morphology is being used in grading the severity and progression of diabetic retinopathy. An image analysis system can assist ophthalmologist make accurate diagnosis in an efficient manner. In this paper, the development of an image processing algorithm for detecting and reconstructing of retinal vasculature is presented. The detection of the vascular structure is achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using Bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature, a region growing technique based on first-order Gaussian derivative is developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enable the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcomes poor performance of current seed-based methods especially in low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database shows that it outperforms many other published methods and achieved an accuracy (ability to detect both vessel and non-vessel pixels) range of 0.91-0.95, a sensitivity (ability to detect vessel pixels) range of 0.91-0.95 and a specificity (ability to detect non-vessel pixels) range of 0.88-0.94.