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Diabetic Retinopathy is a primary cause of blindness in people with diabetes. An automated diabetic retinopathy screening system can be developed. On retina photographs of both damaged and healthy people, a neural network can be trained. This research will determine whether or not the patient has retinopathy.
The rapid development and proliferation of medical imaging technologies is revolutionizing medicine. Medical imaging allows scientists and physicians to glean potentially life-saving information by peering noninvasively into the human body. With medical imaging playing an increasingly prominent role in the diagnosis and treatment of disease, the medical image analysis community has become preoccupied with the challenging problem of extracting, with the assistance of computers, clinically useful information about anatomic structures imaged through Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and other modalities. Image Segmentation, which aims at automated extraction of object boundary features, plays a fundamental role in understanding image content for searching and mining in medical image archives. Manual segmentation is often impractical for large scalar longitudinal projects. The development of robust image segmentation methods is necessary to overcome the limitations of manual segmentation. With the increasing size and number of medical images, the use of computers in facilitating their processing and analysis has become necessary. In particular, computer algorithms for the delineation of anatomical structures and other regions of interest are a key component in assisting and automating specific radiological tasks. These algorithms, called Image segmentation algorithms, play a vital role in numerous biomedical imaging applications such as the quantification of tissue volumes, diagnosis, localization of pathology, study of anatomical structure, treatment planning, partial volume correction of functional imaging data, and computer integrated surgery. The automated methods and procedures described in this work are motivated by the need to segment retinal images. The implementation of these tools, however, is suitable for more general segmentation problems that could include any imaging modalities or segmentation targets. Manual segmentation of the retinal blood vessels is arduous and time-consuming. Thus, automated segmentation is valuable as it decreases the time and effort required. Mostly, the algorithms for retinal blood vessel segmentation concentrate on automatic detection related to diabetic retinopathy, which is found to be the major cause of blindness in recent days.
The principal objective of this course is to provide an introduction to basic concepts and techniques for medical image processing and to promote interests for further study and research in medical imaging processing. Also to develop computational methods and algorithms to analyze and quantify biomedical data. The increasing number of images and the desire to reduce the human subjectivity, encourage the development and improvement of image processing and analysis algorithms. Manual segmentation of the retinal blood vessels is arduous and time-consuming. Thus, automated segmentation is valuable as it decreases the time and effort required. One of the difficulties in image capture of the ocular fundus is image quality, which is affected by factors, such as medial opacities, defocus or presence of artifact. Micro aneurysms (MAs) are the earliest clinical sign of Diabetic Retinopathy. MA detection at early stage can help to reduce the blindness. Our Project work is automatic detection for diabetic retinopathy using non-dilated retinal images at early stages. Our project proposes an automated system to identify diabetic affected eye among the several input retinal images. This is carried out in three stages namely Pre-Processing, Segmentation and Disease Abnormalities Detection. The Proposed method is evaluated using performance metrics.
Human eye isthe light-sensitive organ that enables one to see the surrounding environment. It can be compared to a camera in a sense that the image is formed on the retina of eye while in a traditional camera the image is formed on a film. The cornea and the crystalline lens of the human eye are equivalent to the lens of a camera and the iris of the eye works like the diaphragm of a camera, which controls the amount of light reaching the retina by adjusting the size of pupil. The light passing through cornea, pupil and the lens reaches the retina at the back of the eye, which contains the light sensitive photoreceptors. The image formed on the retina is transformed into electrical impulses and carried to the brain through the optic nerves, where the signals are processed and the sensation of vision is generated. The general diagram of human eye isshown in Figure 1. Fig 2.1: The structure of the human eye. The small, yellowish central area of the retina, which is around 5.5 mm in diameter, is known as macula. The macula and its center area (fovea) provide sharp central vision. A healthy macula can provide at least a normal (20/20) vision. Fovea is densely populated with ‘cone’ photoreceptors, which are responsible for the trichromatic human color vision. Fovea contains no ‘rod’ photoreceptors, which provide information on brightness. The L, M and S-cone cells are sensitive to long, middle, and short wavelength ranges in the visible part of the electromagnetic spectrum (i.e., 380-780 nm), respectively, whereas rod cells provide no color information. Optic disc is the visible part of the optic nerve where the optic nerve fibers and blood vessels enter the eye. It does not contain any rod or cone photoreceptors, so it cannot respond to light.
Diabetic retinopathy (DR) is a complication of diabetes mellitus and the second most common cause ofblindness and visual loss in the world and the most important cause in the working age population. The number of patients with diabetes is increasing rapidly and in 2007 reached 23.5 million. There is abundant evidence that blindness and visual loss in these patients can be prevented through annual screening and early diagnosis.
Modern digital technology has made it possible to manipulate multi-dimensional signals with systems that range from simple digital circuits to advanced parallel computers. The goal of this manipulation can be divided into three categories: • Image Processing image in → image out • Image Analysis image in → measurements out • Image Understanding image in → high-level description out. We will focus on the fundamental concepts of image processing. Beginning with certain basic definitions. An image defined in the “real world” is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y). An image may be considered to contain sub-images sometimes referred to as regions– of–interest, ROIs, or simply regions. This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition.