Comparative Analysis of Improved of FCM Algorithms in the Segmentation of Retinal Blood Vessels

The main purpose of identifying and locating the vessels of the retina is to specify the various tissues from the vascular structure of the retina (which could be differencied between wide or tight) of the background of the fundus image. There exist several segmentation techniques that are spreading to divide the retinal vessels, depending on the issues and complexity of the retinal images. Fuzzy c-means is one of the most often used algorithms for retinal image segmentation due to its effectiveness and speed. This paper analyzes the performance of improved FCM algorithms for retinal image segmentation in terms of their ability and capability in segmenting and isolating blood vessels. The process we followed in our paper consists of two phases. Firstly, the pre-processing phase, where the green channel is taken for the color image of the retina. Contrast enhancement is performed through CLAHE , proceeded by applying bottom-hat filtering to bottom-hat filtering is applied with the purpose to define the region of interest. Secondly, in the segmentation phase the obtained image is segmented using FCM algorithms. The algorithms chosen for this study are: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and, SSFCM performed on DRIVE and STARE databases. Experiments accomplished on DRIVE and STARE databases demonstrate that the DSFCM_N algorithm achieves better results on the DRIVE database, whereas the FGFCM algorithm provides better results on the STARE database in term of accuracy. Concerning time consumption. The FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images.

An inherited night blindness in Wiltshire sheep

Twelve cases of adult-onset blindness were identified in a flock of 130 polled Wiltshire sheep in New Zealand over a 3-year period. Affected sheep developed night blindness between 2 and 3 years of age, which progressed to complete blindness by 4 to 5 years of age. Fundic examination findings included progressive tapetal hyperreflectivity and attenuation of retinal blood vessels. Histologically, the retinas had a selective loss of rod photoreceptors with initial preservation of cone photoreceptors. Retinal degeneration was not accompanied by any other ocular or central nervous system abnormalities, and pedigree analysis suggested an inherited basis for the disease. Mating an affected Wiltshire ram to 2 affected Wiltshire ewes resulted in 6 progeny that all developed retinal degeneration by 2 years of age, while mating of the same affected ram to 6 unaffected ewes resulted in 8 unaffected progeny, consistent with autosomal recessive inheritance. Homozygosity mapping of 5 affected Wiltshire sheep and 1 unaffected Wiltshire sheep using an OvineSNP50 Genotyping BeadChip revealed an identical-by-descent region on chromosome 5, but none of the genes within this region were considered plausible candidate genes. Whole-genome sequencing of 2 affected sheep did not reveal any significant mutations in any of the genes associated with retinitis pigmentosa in humans or progressive retinal atrophy in dogs. Inherited progressive retinal degeneration affecting rod photoreceptors has not been previously reported in sheep, but this disease has several similarities to inherited retinal dystrophies in other species.

DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

Case Report: Glaucoma in an Infant With Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness that occurs due to incomplete development of retinal blood vessels in preterm infants. Glaucoma is an ocular comorbidity in some patients with ROP, and it may be associated with immature anterior chamber development, ROP itself, or the treatment for ROP. There have been a few reports of narrow-angle glaucoma after laser treatment for ROP. In this case report, we describe the course of a female infant born at 24 weeks and 5 days of gestational age with treatment-requiring ROP treated with laser photocoagulation who subsequently developed very elevated intraocular pressure and shallow anterior chambers without pupillary block. The patient required bilateral ab externo trabeculotomy for elevated intraocular pressure, which normalized after the procedure. The patient has remained stable at the last follow-up at 51 weeks postmenstrual age. Differing from previous glaucoma presentations in this setting, we illustrate a case of elevated intraocular pressure and anterior chamber narrowing after laser therapy without pupillary block or synechiae. The possible multifactorial etiology of glaucoma in this patient, including incomplete angle development, ischemia, and laser treatment, highlight the need for glaucoma screening in patients with ROP, both in the short and long term.

Retinal Blood Vessel Extraction Using a New Enhancement Technique of Modified Convolution Filters and Sauvola Thresholding

The retinal blood vessels in humans are major components with different shapes and sizes. The extraction of the blood vessels from the retina is an important step to identify the type or nature of the pattern of the diseases in the retina. Furthermore, the retinal blood vessel was also used for diagnosis, detection, and classification. The most recent solution in this topic is to enable retinal image improvement or enhancement by a convolution filter and Sauvola threshold. In image enhancement, gamma correction is applied before filtering the retinal fundus. After that, the image should be transformed to a gray channel to enhance pictorial clarity using contrast-limited histogram equalization. For filter, this paper combines two convolution filters, namely sharpen and smooth filters. The Sauvola threshold, the morphology, and the medium filter are applied to extract blood vessels from the retinal image. This paper uses DRIVE and STARE datasets. The accuracies of the proposed method are 95.37% for DRIVE with a runtime of 1.77[Formula: see text]s and 95.17% for STARE with 2.05[Formula: see text]s runtime. Based on the result, it concludes that the proposed method is good enough to achieve average calculation parameters of a low time quality, quick, and significant.

Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network

The accurate segmentation of retinal blood vessels in fundus is of great practical significance to help doctors diagnose fundus diseases. Aiming to solve the problems of serious segmentation errors and low accuracy in traditional retinal segmentation, a scheme based on the combination of U-Net and Dense-Net was proposed. Firstly, the vascular feature information was enhanced by fusion limited contrast histogram equalization, median filtering, data normalization and multi-scale morphological transformation, and the artifact was corrected by adaptive gamma correction. Secondly, the randomly extracted image blocks are used as training data to increase the data and improve the generalization ability. Thirdly, stochastic gradient descent was used to optimize the Dice loss function to improve the segmentation accuracy. Finally, the Dense-U-net model was used for segmentation. The specificity, accuracy, sensitivity and AUC of this algorithm are 0.9896, 0.9698, 0.7931, 0.8946 and 0.9738, respectively. The proposed method improves the segmentation accuracy of vessels and the segmentation of small vessels.

The Determination of Type 2 Diabetes Mellitus’s Impact on the Density of Retinal Blood Vessels and the Choriocapillaris: Optical Coherence Tomography Angiography Study

Optical coherence tomography angiography (OCTA) was used to analyze the alterations in the density of retinal blood vessels and the choriocapillaris (VD) in patients suffering from type 2 diabetes mellitus (T2DM). One hundred sixty-six eyes of 83 patients (43 of whom were men and 40 women, with a mean age of 58.59 ± 14.04) with T2DM and without diabetic retinopathy were examined for the purpose of conducting the observational prospective study. The control group (CG) consisted of 66 eyes in 33 healthy subjects (15 male and 18 female, with a mean age of 55.12 ± 12.70). The measurement regions of vessel density (VD) included the deep capillary plexus (DCP), the superficial capillary plexus (SCP), and the choriocapillaris. The results indicate considerable differences in the VD of the DCP and SCP when comparing the control group with the study groups ( p < 0.001 ). In comparison with the control group ( p < 0.001 ), there was a statistically significant reduction in the VD of the choriocapillaris in the study group. Furthermore, patients with T2DM showed a significantly decreased VD concerning the control in different macular regions. Thickness in several macular regions in the study group significantly decreased compared to the ones in the control group. OCTA was used to gather relevant information about the vascular changes which occurred in T2DM patients, assessed through the quantitative analysis of the blood flow in the retina and choriocapillaris.

Research progress of UWFFA and OCTA in retinal vein occlusion: A review

Retinal vein occlusion (RVO) is a retinal vascular disease that severely impairs the visual function of patients. Observing the changes of retinal blood vessels before and after treatment is of great significance for the prognostic evaluation of RVO. The rapid development and widespread use of fundus imaging technique, especially ultra-wide-angle fundus fluorescein angiography (UWFFA) and optical coherence tomography angiography (OCTA) have made our observation of the retinal blood vessels of RVO more comprehensive and meticulous. In this paper, we reviewed the latest research progress of UWFFA and OCTA in RVO.