A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Cotton Wool Spot Detection. <s> purpose. To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. methods. Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagnosis database (nonmydriatic camera, two-field photography): 100 with previously diagnosed bright lesions and 200 without. A machine learning computer program was developed that can identify and differentiate among drusen, (hard) exudates, and cotton-wool spots. A human expert standard for the 300 images was obtained by consensus annotation by two retinal specialists. Sensitivities and specificities of the annotations on the 300 images by the automated system and a third retinal specialist were determined. results. The system achieved an area under the receiver operating characteristic (ROC) curve of 0.95 and sensitivity/specificity pairs of 0.95/0.88 for the detection of bright lesions of any type, and 0.95/0.86, 0.70/0.93, and 0.77/0.88 for the detection of exudates, cotton-wool spots, and drusen, respectively. The third retinal specialist achieved pairs of 0.95/0.74 for bright lesions and 0.90/0.98, 0.87/0.98, and 0.92/0.79 per lesion type. conclusions. A machine learning-based, automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed and approaches the performance level of retinal experts. If the machine learning can be improved with additional training data sets, it may be useful for detecting clinically important bright lesions, enhancing early diagnosis, and reducing visual loss in patients with diabetes. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Cotton Wool Spot Detection. <s> Purpose Patients infected with the human immunodeficiency virus (HIV) develop noninfectious retinopathy characterized by retinal cotton wool spots (CWS) and microvascular abnormalities. Ophthalmoscopically, CWS fade with time. We hypothesized that structural changes should be permanent and possibly visible well after ophthalmoscopic resolution. We used simultaneous spectral domain optical coherence tomography (SD-OCT)/scanning laser ophthalmoscope (SLO) to allow colocalization of the lesions and determine the extent and location of residual damage after ophthalmoscopic resolution of the lesions. Design Retrospective, noninterventional case series. Participants Eight eyes of 7 HIV patients with 19 resolved retinal CWS. Methods Nineteen retinal CWS were imaged between 2 and 16 years (median, 7.84) after the acute lesions using simultaneous SD-OCT and SLO examinations. The areas of the previous CWS were scanned by overlaying the color retinal image over the SLO image and scanning at high resolution in the horizontal plane through the resolved lesion. Each CWS lesion had a control area taken from the same eye within 2 disc diameters of the lesion. The thickness of each of the retinal layers was compared between lesions and control areas using a paired t -test with multitest correction. Main Outcome Measures Thickness of the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), and outer nuclear layer (ONL). Results The greatest loss of thickness was seen in the retinal GCL with a 43% reduction in thickness. There was a statistically significant thinning of the RNFL, GCL, IPL, INL, and OPL. The median thickness differences ranged from 5 to 7 microns. This difference was highly significant. Another striking finding was the displacement of the ONL toward the retinal surface resulting in an apparent increase in thickness of the ONL by >15% (median difference, 12 microns). Conclusions Our data, using ultrahigh resolution and high-speed SD-OCT/SLO, show and quantify the presence of permanent retinal destruction associated with retinal CWS in HIV disease. Financial Disclosure(s) The authors have no proprietary or commercial interest in any of the materials discussed in this article. <s> BIB002 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Cotton Wool Spot Detection. <s> The damage to the retina of the eye caused by high blood pressure is known as hypertensive retinopathy (HR). The advancement in automatic retinal image analysis has made it possible to detect & diagnose this disease at an early stage. To indicate the presence and severity of Hypertensive Retinopathy, Arterio-Venous ratio (AVR) is an important measurement. For the calculation of AVR (the ratio of retinal arterioles to venuoles), classification of vessels into arteries and veins is an essential step. This paper proposes a novel technique for classifying the vessels into arteries and veins. The proposed system uses binary vessel mask and optic disc (OD) center localization to extract Region of Interest (ROI) around OD. Feature extraction modules extract a number of intensity based features followed by classification which classifies vessels as arteries and veins. The features are extracted by focusing on the visual difference between the arteries and veins in different color spaces. The proposed system is tested on a locally gather fundus image dataset. An accuracy of 81.3% is achieved. <s> BIB003
In the fundoscopic test of the retina of human eyes, Cotton Wool Spots (CWS) are found as the abnormality which looks like a white fluffy patch. They are also considered as the retinal infarcts which mean the damage of tissues as a result of insufficient oxygen supply because of the blockage of blood supply to the tissues and this mostly happens in the retina of the diabetic patient that leads them to be in risk of acute stroke BIB003 . The configuration of CWS is found similar in the fundus eye images of both diabetes and hypertension patient ( BIB002 ; Irshad & Akram, 2015; ) . Figure 4 displays the CWS in fundus retinal image inside the black circle. Researchers of BIB001 developed an automated system based on machine learning to detect the CWS and differentiate this from drusen existing in color images that were collected from diabetic patients and the researchers were able to obtain the sensitivity pairs of 0.95/0.86 to detect CWS.