Optic Disk Segmentation Using Histogram Analysis

In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR databas

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)

Automatic Blood Vessel Extraction of Fundus Images Employing Fuzzy Approach

Diabetic Retinopathy is a retinal vascular disease that is characterized by progressive deterioration of blood vessels in the retina and is distinguished by the appearance of different types of clinical lesions like microaneurysms, hemorrhages, exudates etc. Automated detection of the lesions plays significant role for early diagnosis by enabling medication for the treatment of severe eye diseases preventing visual loss. Extraction of blood vessels can facilitate ophthalmic services by automating computer aided screening of fundus images. This paper presents blood vessel extraction algorithms with ensemble of pre-processing and post-processing steps which enhance the image quality for better analysis of retinal images for automated detection. Extensive performance based evaluation of the proposed approaches is done over four databases on the basis of statistical parameters. Comparison of both blood vessel extraction techniques on different databases reveals that fuzzy based approach gives better results as compared to Kirsch’s based algorithm. The results obtained from this study reveal that 89% average accuracy is offered by the proposed MBVEKA and 98% for proposed BVEFA

Intellectual System Diagnostics Glaucoma

Glaucoma is a chronic eye disease that can lead to permanent vision loss. However, glaucoma is a difficult disease to diagnose because there is no pattern in the distribution of nerve fibers in the ocular fundus. Spectral analysis of the ocular fundus images was performed using the Eidos intelligent system. From the ACRIMA eye image database, 90.7% of healthy eye images were recognized with an average similarity score of 0.588 and 74.42% of glaucoma eye images with an average similarity score of 0.558. The reliability of eye image recognition can be achieved by increasing the number of digitized parameters of eye images obtained, for example, by optical coherence tomography. The research contribution is the digital processing of fundus graphic images by the intelligent system “Eidos”. The scientific contribution lies in the automation of the glaucoma diagnosis process using digitized data. The results of the study can be used at medical faculties of universities to carry out automated diagnostics of glaucoma

Intellectual System Diagnostics Glaucoma

Glaucoma is a chronic eye disease that can lead to permanent vision loss. However, glaucoma is a difficult disease to diagnose because there is no pattern in the distribution of nerve fibers in the ocular fundus. Spectral analysis of the ocular fundus images was performed using the Eidos intelligent system. From the ACRIMA eye image database, 90.7% of healthy eye images were recognized with an average similarity score of 0.588 and 74.42% of glaucoma eye images with an average similarity score of 0.558. The reliability of eye image recognition can be achieved by increasing the number of digitized parameters of eye images obtained, for example, by optical coherence tomography. The research contribution is the digital processing of fundus graphic images by the intelligent system “Eidos”. The scientific contribution lies in the automation of the glaucoma diagnosis process using digitized data. The results of the study can be used at medical faculties of universities to carry out automated diagnostics of glaucoma

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 39)

Abstracts are provided for 154 patents and patent applications entered into the NASA scientific and technical information systems during the period Jan. 1991 through Jun. 1991. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application