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