A comprehensive survey assessing the presence of Acanthamoeba was conducted on 50 samples from water sources in parks and public squares from 22 municipal districts of Tehran, Iran. The prevalence and genotypes of Acanthamoeba were determined by PCR and the PCR fragments of ribosomal RNA genes sequenced. Sixteen (32%) samples were positive for Acanthamoeba spp.Sequence analysis revealed that the positive isolates belonged to the T4 and T5 genotypes. Fourteen isolates (87.5%) were T4, and two (12.5%) were T5. Acanthamoeba may be a problematic organism for contact lens wearers and for immunocompromised individuals. In Iran, Acanthamoeba keratitis has increased in recent years, mainly due to poor hygiene in contact lens wearers. A thorough survey for the prevalence of this amoeba could have a significant role in prevention of disease. This is the first report of the T5 genotype from water in recreational areas of Tehran.

A survey was conducted to determine the presence of free-living amoebae (FLA), especially Acanthamoeba and Naegleria, in river recreation areas in Tehran Province, Iran. All rivers surveyed were associated with human activity, and two were also a source of municipal tap water. Fifty-five water samples from 10 major rivers were screened for FLA and identified by morphological characters, PCR amplification targeting specific genes for Acanthamoeba (DF3 region of Rns gene) and other FLA (ITS PCR), and homology analysis. The percentage of positive FLA isolates was 27.3%, of which 80% were Acanthamoeba, assigned to the T4 and T15 genotype, and 20% were Naegleria.Isolation of Acanthamoeba T4 genotype (91.7%) from recreation areas could be a health threat and a sanitary risk associated with human activity where young people and tourists congregate in summer.Posting of warning signs and education of high-risk individuals are important for disease prevention.To the best of our knowledge this is the first report of genotype T15 (clustered with A. jacobsi) identified in Iran and the first report of the distribution of FLA such as Naegleria (N. pagei, N. clarki and N. fultoni) in recreation areas in rivers of Tehran Province using molecular methods.

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Computer-Aided Diagnosis systems are required to extract suitable information about retina and its changes. In particular, identifying objects of interest such as lesions and anatomical structures from the retinal images is a challenging and iterative process that is doable by image processing approaches. Microaneurysm (MAs) are one set of these changes that caused by diabetic retinopathy (DR). In fact, MAs detection is the main step for identification of DR in the retinal images analysis. The objective of this study is to apply an automated method for detection of MAs and compare the results of detection with and without vessel segmentation and masking either in the normal or abnormal image. The steps for the detection and segmentation are as follows. At the first step, we did preprocessing, by using top-hat transformation. Our main processing was included applying Radon transform, to segment the vessels and masking them. At last, we did MAs detection step using combination of Laplacian-of-Gaussian and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth that collected by ophthalmologists. With vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs with 11 false positive rate per image for 100 color images in a local retinal database and 20 images of a public dataset (DRIVE). Also without vessel segmentation, our automated algorithm finds sensitivity of about 90% in detection of MAs with 73 false positives per image for all 120 images of two databases. In conclusion, with vessel segmentation we have acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural databse (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them.