Blood vessels detection and segmentation in retina using gabor filters

Currently, medical images have become an important field of research due to the progress in their acquisition, storage and management in a wide range of applications. In the medical domain, a huge effort has been devoted to develop applications and tools related with disease diagnosis and identification of anatomical structures. Spatially, the extraction of the vessel pattern from retinal images has an important issue not only in the medical tools; but also in biometric identification applications. A lot of research has been devoted to accurately extract the retinal vessels in order to define applications for early ocular disease diagnosis. The main regions of retina are optic disk, fovea and blood vessels. The identification of these areas can help in analyses of the diseases that affect these regions such as retinopathy of prematurity, glaucoma and diabetic retinopathy. In earlier studies the Gabor filters have received considerable attention because of the characteristics of certain cells in the visual cortex of some mammals can be defined by these filters. In addition, Gabor has shown that the representation of signals using the Gaussian functions modulated by complex exponentials is optimal in the sense of minimizing the joint uncertainty in the combined time-frequency domain. Therefore, these functions provide the best trade-off between time resolution and frequency resolution. In this thesis we propose to use Gabor filtering technique for the detection of blood vessels in retina. For retina images we used 180 different filters, with the rotation separation of 1 degree to capture for more edge information of the vessels. Different scales are also used to include edge information of the vessels. After Gabor filtering image processing methods including logarithmic normalization, morphological erosion, mask processing and thresholding are used to perform the segmentation of the detected vessels. The proposed approach is applied to retina images from publicly available databases which is the Digital Retinal Images for Vessel Extraction database (DRIVE). Manually segmented versions of these images along with masks are used for performance analysis. Receiver Operating Characteristic (ROC) Curves containing false acceptance rate (FAR) and false rejection rate (FRR) are incorporated to systematically determine a suitable threshold for reliable segmentation performance. Sensitivity, specificity, efficiency and accuracy of the retina vessels segmentation have been studied and the generated results are comparable with alternative state-of-the-art methods available in the relevant literature. The results show that the employed method produces comparable and sometimes better results than the alternative methods available in the literature. Keywords: Digital Image Processing, Gabor Filters, Detection of Blood Vessels, Edge Detection, Morphological Filters, Retinal Fundus Images