A Rule Based Segmentation Approaches to Extract Retinal Blood Vessels in Fundus Image

The physiological structures of the retinal blood vessel are one of the key features that visible in the retinal images and contain the information associate with the anatomical abnormalities. It is accepted all over the world to judge the cardiovascular and retinal disease. To avoid the risk of visual impairment, appropriate vessel segmentation is mandatory. Here has proposed a segmentation algorithm that efficiently extracts the blood vessels from the retinal fundus image. The proposed segmentation algorithm is performed Lab and Principle Component (PC) based gray level conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations, Local Property-Based Pixel Correction (LPBPC). For appropriate detection proposed vessels correction algorithm LPBPC that check the feature of the vessels and remove the wrong vessel detection. To measure the appropriateness of the proposed algorithm, the experimental results are compared with the corresponding ground truth images. The experimental results have shown that the proposed blood vessel algorithm is more accurate than the existing algorithms

A Comparative Study of Different Blood Vessel Detection on Retinal Images

Detection of blood vessel plays an important stage in different medical areas, such as ophthalmology, oncology, neurosurgery, and laryngology. The significance of the vessel analysis was helped by the continuous overview in clinical studies of new medical technologies intended for improving the visualization of vessels. In this paper, several local segmentation techniques which include such as Vascular Tree Extraction, Tyler L. Coye and Line tracking, Kirsch’s Template and Fuzzy C Mean methods were studied. The main objective is to determine the best approaches in order to detect the blood vessel on the degraded retinal input image (DRIVE dataset). A few Image Quality Assessment (IQA) was obtained to prove the effectiveness of each detection methods. Overall, the result of sensitivity highest came from Kirsch Templates (96.928), while specificity from Fuzzy C means (77.573). However, in term of accuracy average, the Line Tracking method is more successful compared to the other methods

Segmentation Of Retinal Blood Vessels Using A Novel Fuzzy Logic Algorithm

In this work, a rule-based method is presented for blood vessel segmentation in digital retinal images. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness. Diagnosis of diabetic retinopathy at an early stage can be done through the segmentation of the blood vessels of retina. Many studies have been carried out in the last decade in order to obtain accurate blood vessel segmentation in retinal images including supervised and rule-based methods. This method uses eight feature vectors for each pixel. These features are means and medians of intensity values of pixel itself, first and second nearest neighbor at four directions. Features are used in fuzzy logic algorithm as crisp input. The final segmentation is obtained using a thresholding method. The method was tested on the publicly available database DRIVE and its results are compared with distinguished published methods. Our method achieved an average accuracy of 93.82% and an area under the receiver operating characteristic curve of 94.19% for DRIVE database. Our results demonstrated an average sensitivity of 72.28% and a specificity of 97.04%. The calculated sensitivity and specificity values for DRIVE database also state that the proposed segmentation method is effective and robust

A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose an automatic blood vessel segmentation method. The proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the binary images resulting from centerline detection part with the image resulting from fuzzy vessel segmentation part. In this proposed algorithm, the blood vessel is enhanced using modified morphological operations and the salt and pepper noises are removed from retinal images using Adaptive Fuzzy Switching Median filter. This method is applied on two publicly available databases, the DRIVE and the STARE and the experimental results obtained by using green channel images have been presented and compared with recently published methods. The results demonstrate that our algorithm is very effective method to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i4.625

Two Novel Retinal Blood Vessel Segmentation Algorithms

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose two automatic blood vessel segmentation methods. The first proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the contents of several binary images resulting from vessel width dependent modified morphological filters on normalized retinal images. In the second proposed algorithm the blood vessel is segmented using normalized modified morphological operations and neuro fuzzy classifier. Normalized morphological operations are used to enhance the vessels and neuro fuzzy classifier is used to segment retinal blood vessels. These methods are applied on the publicly available DRIVE database and the experimental results obtained by using green channel images have been presented and their results are compared with recently published methods. The results demonstrate that our algorithms are very effective methods to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i3.582

Automatic Detection of Diabetic Retinopathy from Color Fundus Retinal Images

The influence and impact of digital images on modern society is tremendous, and image processing is now a critical component in science and technology, in which Image segmentation plays a crucial role in many medical imaging applic ations. Medical image segmentation has a vital role in diagnosis, surgical planning, navigation, and various medical evaluations. Moreover it is suitable for segmenting the blood vessel of retinal images which is used for automated screening of early diabe tic retinopathy (damage to the retina) detection caused by complications of diabetes mellitus, which can eventually lead to blindness. One of the main challenges in medical image processing is to segment the blood vessel with higher accuracy rate hence we propose a novel technique to increase the accuracy rate of segmenting the blood vessel

A robust lesion boundary segmentation algorithm using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician demarcated boundaries as ground truth