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Biology 代写:Build A Decision Support System Using Retinal Images

Biology 代写:Build A Decision Support System Using Retinal Images

视网膜图像分析是诊断糖尿病视网膜病患者的一个关键因素。影响眼睛眼底的疾病的模式是多种多样的。因此,一个训练有素的观察者如眼科医生需要确定这些模式。通过分析视网膜图像的某些特征,医生尽可能的眼部疾病如糖尿病视网膜病变的视网膜出现诊断(DR)、黄斑变性、青光眼等,主要特点是诊断病情观察主要分为两类:这是亮点和暗点。视网膜图像中的亮点包括视盘,分泌物,和棉花羊毛而暗斑是血管和微动脉瘤,出血。

最原始的视网膜图像对比度低,发生噪声将使诊断过程变得强硬,因为所有的特征都不明显。眼科医生观察特征准确质量差的视网膜图像是很困难的。

据美国国家眼科研究所、失明或视力低下影响330万的美国人40岁及以上。这个数字有望在今年达到550万2020 [ 1 ]。作为视网膜病变病例数量的增加,眼科医生需要分析更多的视网膜图像,增加他们的工作量。医生可能需要一个能够帮助他们提高诊断过程的效率系统。

因此,在这个项目中,我们将建立一个基于视网膜图像的疾病诊断过程的决策支持系统。

Retinal image analysis is a key element in diagnosis retinopathies in patients. The patterns of disease that affect the fundus of the eye are varied. Therefore, a trained human observer such as an ophthalmologist is required to identify these patterns. By analyze some features in a retinal images, ophthalmologist can diagnosis the possible ocular disease that occur in the retinal such as diabetes retinopathy (DR), macular degeneration, glaucoma and etc. Main features to be observed for diagnosis disease are divided into two categories which are bright spots and dark spots. The bright spots in retinal images consist of Optic disc, exudates, and cotton wool while dark spots are blood vessels, haemorrhages, and microaneurysm.

Most of the original retinal images are low contrast and have occurrence of noise that will makes the diagnosis process become tougher because all of the features are unobvious. It is hard for an ophthalmologist to observe a feature accurately in poor quality retinal images.

According to the National Eye Institute, blindness or low vision affects 3.3million Americans age 40 and over. This figure is expected to reach 5.5million by the year 2020 [1]. As the increasing numbers of retinopathy cases, ophthalmologists need to analyze more retinal images which increase their workload. Ophthalmologist might need a system which can assist them to improve the efficiency of diagnosis process.

Therefore, in this project, we are going to build a Decision Support System based on Retinal Images for disease diagnosis process.

Introduction

Project Scopes

Pre-processing on retinal images to enhance the local contrast and reduce the noises in the image. The pre-processing can improve the obviousness of important features in bright spot such as optic disk, exudates and cotton wool spots.

Detect and extract the features in bright spot of retinal images which are optic disk, exudates and cotton wool spots.

Present each main feature in separate images in user interface.

Analyze on each feature based on rules of diagnosis to decide whether it is normal or abnormal.

Diagnosis whether any ocular disease is proven based on the analyzed features.

A simple and user friendly interface is necessary for this system because the user might not familiar in programming code.

Objective

To build a decision support system using retinal images for disease diagnosis such as Diabetes Retinopathy. This system will figure out the possible ocular disease based on the features in retinal images.

To process on retinal images using Matlab Image Tools for extracting the features needed in disease diagnosis process. These features include optic disk, exudates, and cotton wool which are the bright spot in the retinal image.

To help the ophthalmologist to improve their productivity, efficiency and cost effective in the disease diagnosis process. Ophthalmologist can identify each feature by using this system rather than manual diagnosis by analyze using the original retinal images which features are not shown obviously.

To do research on methodologies for processing retinal images such as detection and extraction of optic disk, exudates, and cotton wool. Besides, research on certain ocular disease and the rules of diagnosis these diseases are needed in this project.

1.5 Gantt Chart

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