Retinal Blood Vessel Segmentation Algorithm for Diabetic Retinopathy using Wavelet: A Survey

Blood vessel structure in retinal images have an important role in diagnosis of diabetic retinopathy. There are several method present for automatic retinal vessel segmentation. For developing retinal screening systems blood vessel segmentation is the basic foundation since vessels serve as one of the main retinal landmark features. The most common signs of diabetic retinopathy include hemorrhages, cotton wool spots, dilated retinal veins, and hard exudates. A patient with diabetic retinopathy disease has to undergo periodic screening of eye. For the diagnosis, doctors use color retinal images of a patient required from digital fundus camera. We present a method that uses Gabor wavelet for vessel enhancement due to their ability to enhance directional structures and euclidean distance technique for accurate vessel segmentation. Retinal angiography images are mainly used in the diagnosis of diseases such as diabetic retinopathy and hypertension etc. In diabetic retinopathy structure of retinal blood vessels change that leads to adult blindness. To overcome this problem automatic biomedical diagnosis system is required.The main stage of diabetic retinopathy are Non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Eye care specialist can screen vessel abnormalities using an efficient and effective computer based approach to the automated segmentation of blood vessels in retinal images. Automated segmentation reduces the time required by a physician or a skilled technician for manual labeling. Thus a reliable method of vessel segmentation would be valuable for the early detection and characterization of changes due to such diseases. This article presents the automated vessel enhancement and segmentation technique for colored retinal images. Segmentation of blood vessels from image is a difficult task due to thin vessels and low contrast between vessel edges and background. The proposed method enhances the vascular pattern using Gabor wavelet and then it uses euclidean distance technique to generate gray level segmented image. DOI: 10.17762/ijritcc2321-8169.15030

Retinal vessel segmentation using Gabor Filter and Textons

This paper presents a retinal vessel segmentation method that is inspired by the human visual system and uses a Gabor filter bank. Machine learning is used to optimize the filter parameters for retinal vessel extraction. The filter responses are represented as textons and this allows the corresponding membership functions to be used as the framework for learning vessel and non-vessel classes. Then, vessel texton memberships are used to generate segmentation results. We evaluate our method using the publicly available DRIVE database. It achieves competitive performance (sensitivity=0.7673, specificity=0.9602, accuracy=0.9430) compared to other recently published work. These figures are particularly interesting as our filter bank is quite generic and only includes Gabor responses. Our experimental results also show that the performance, in terms of sensitivity, is superior to other methods

Optic disc detection by earth mover's distance template matching

This paper presents a method for the detection of OD in the retina which takes advantage of the powerful preprocessing techniques such as the contrast enhancement, Gabor wavelet transform for vessel segmentation, mathematical morphology and Earth Mover’s distance (EMD) as the matching process. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Vessel segmentation method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel’s feature vector. Feature vectors are composed of the pixel’s intensity and 2D Gabor wavelet transform responses taken at multiple scales. A simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity using the EMD. The minimum distance provides an estimate of the OD center coordinates. The method’s performance is evaluated on publicly available DRIVE and STARE databases. On the DRIVE database the OD center was detected correctly in all of the 40 images (100%) and on the STARE database the OD was detected correctly in 76 out of the 81 images, even in rather difficult pathological situations

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0.9541 on DRIVE database and 0.9568, 0.9478 and 0.9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively

Robust methodology for fractal analysis of the retinal vasculature

We have developed a robust method to perform retinal vascular fractal analysis from digital retina images. The technique preprocesses the green channel retina images with Gabor wavelet transforms to enhance the retinal images. Fourier Fractal dimension is computed on these preprocessed images and does not require any segmentation of the vessels. This novel technique requires human input only at a single step; the allocation of the optic disk center. We have tested this technique on 380 retina images from healthy individuals aged 50+ years, randomly selected from the Blue Mountains Eye Study population. To assess its reliability in assessing retinal vascular fractals from different allocation of optic center, we performed pair-wise Pearson correlation between the fractal dimension estimates with 100 simulated region of interest for each of the 380 images. There was Gaussian distribution variation in the optic center allocation in each simulation. The resulting mean correlation coefficient (standard deviation) was 0.93 (0.005). The repeatability of this method was found to be better than the earlier box-counting method. Using this method to assess retinal vascular fractals, we have also confirmed a reduction in the retinal vasculature complexity with aging, consistent with observations from other human organ systems

Blood Vessel Enhancement and Segmentation for Screening of Diabetic Retinopathy

Diabetic retinopathy is an eye disease caused by the increase of insulin in blood and it is one of the main cuases of blindness in idusterlized countries. It is a progressive disease and needs an early detection and treatment. Vascular pattern of human retina helps the ophthalmologists in automated screening and diagnosis of diabetic retinopathy. In this article, we present a method for vascular pattern ehnacement and segmentation. We present an automated system which uses wavelets to enhance the vascular pattern and then it applies a piecewise threshold probing and adaptive thresholding for vessel localization and segmentation respectively. The method is evaluated and tested using publicly available retinal databases and we further compare our method with already proposed techniques.

RETINAL MICROVASCULATURE EXTRACTION USING GABOR WAVELET

This project discusses diabetic retinopathy which contributes to adult major vision loss. It is caused by abnormal changes in blood vessels on retina.[1] Therefore, a system is developed to enable early inspection. Using 2-D Gabor wavelet vascular pattern is enhanced and further classified by comparing the result from a set of database available on DRIVE