本文關(guān)鍵詞： 糖尿病視網(wǎng)膜病變 眼底圖像 硬性滲出物 背景估計 集成分類 深度學習　出處：《哈爾濱工業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著人類生產(chǎn)生活方式、飲食習慣的變化以及生活水平的提高,糖尿病已經(jīng)成為世界范圍內(nèi)廣泛影響人們身體健康的慢性疾病。糖尿病患者由于缺乏胰島素或細胞對胰島素抵抗作用異常而產(chǎn)生高血糖癥狀。持續(xù)的高血糖打亂了機體正常的代謝活動,造成代謝功能紊亂,從而誘發(fā)多種并發(fā)癥。其中,糖尿病視網(wǎng)膜病變作為糖尿病最嚴重的并發(fā)癥之一,已成為導致成年人視力受損甚至失明的主要原因之一。因此,對糖尿病視網(wǎng)膜病變的早期診斷與及時對癥治療對降低患者的失明風險具有重要意義。目前,對糖尿病視網(wǎng)膜病變的篩查主要通過眼科醫(yī)師人工檢查患者的眼底圖像來實現(xiàn),這種檢查方式效率較低并且很大程度上依賴于眼科醫(yī)師的臨床經(jīng)驗。因此,研究眼底圖像病變自動檢測技術(shù),實現(xiàn)對眼底病變客觀、高效、準確的檢測具有積極意義和實用價值。硬性滲出物的出現(xiàn)是糖尿病視網(wǎng)膜病變的早期癥狀,所以可以通過對硬性滲出物的檢測實現(xiàn)對糖尿病視網(wǎng)膜病變的早期檢測與診斷。本文對眼底硬性滲出物自動檢測技術(shù)的研究主要包括兩個方面:基于傳統(tǒng)計算機視覺技術(shù)的檢測方法和基于深度學習的檢測方法。其中基于傳統(tǒng)計算機視覺技術(shù)的檢測方法可以劃分為硬性滲出物粗分割階段和硬性滲出物細分類階段。在粗分割階段,提出利用形態(tài)學方法進行背景估計,并結(jié)合形態(tài)學重建和閾值化技術(shù)初步分割得到含有硬性滲出物的區(qū)域。在細分類階段,引入了區(qū)域塊特征和區(qū)域結(jié)構(gòu)特征等描述子,提取了恰當?shù)膮^(qū)域特征向量,利用集成分類方法構(gòu)建的裝袋決策樹對粗分割階段得到的區(qū)域進行進一步分類,得到最終的硬性滲出物檢測結(jié)果�；谏疃葘W習的方法分別研究了傳統(tǒng)的分類網(wǎng)絡(luò)和生成對抗網(wǎng)絡(luò),并對生成對抗網(wǎng)絡(luò)進行了一定程度的改進,使其更適于進行分類,最終實現(xiàn)了使用生成對抗網(wǎng)絡(luò)對眼底圖像的硬性滲出物進行像素級的檢測。本文在公開的眼底圖像數(shù)據(jù)集DIARETDB1上對提出的自動檢測算法進行了測試,得到了敏感性98.08%,特異性94.39%,陽性預測值94.07%,準確率95.77%的結(jié)果。與其他的相關(guān)研究工作進行比較后可以發(fā)現(xiàn)本文提出的方法具有一定的優(yōu)越性和潛在的應(yīng)用價值。
[Abstract]:With the development of human production and life style, changes in eating habits and the improvement of living standards, diabetes has become a world wide range of chronic diseases affecting people's health. Diabetic patients due to the lack of effect and abnormal symptoms of hyperglycemia on insulin or insulin resistance cells. Sustained high blood glucose disrupted the normal metabolic activities of the human body, causing metabolic function disorder, which cause a variety of complications. The diabetic retinopathy is one of the most serious complications of diabetes, has become one of the major causes of adult visual impairment and even blindness. Therefore, the early diagnosis of diabetic retinopathy and timely symptomatic treatment is of great significance to reduce the patient's risk of blindness. At present, the screening of diabetic retinopathy mainly through the fundus image examination in patients with artificial ophthalmologists to achieve this. Check the clinical experience of low efficiency and rely heavily on the ophthalmologist. Therefore, the automatic detection technology on pathological fundus images, to achieve the objective of retinopathy, high efficiency, has the positive significance and practical value for accurate detection. Hard exudation is the early symptoms of diabetic retinopathy, so by early detection and diagnosis of diabetic retinopathy detection of hard exudates. The fundus hard exudates automatic detection technology research mainly includes two aspects: the traditional detection methods of computer vision technology and detection method based on deep learning. Based on the traditional detection method based on computer vision technology can be divided into hard exudates the coarse segmentation stage and exudate subdivision stage. In the stage of coarse segmentation, using morphological method to estimate the background Meter, and combined with morphological reconstruction and threshold segmentation technology initially contain hard exudates area. In the classification stage, introduced the region characteristics and regional structural characteristics and regional feature vector descriptor extraction appropriate, further classification decision tree is constructed using the bag integrated classification method to get the coarse segmentation stage area, get the final detection results of hard exudates. Method of deep learning classification based on network and traditional network against generation were studied, and the formation of combat network was improved to a certain extent, so it is more suitable for the classification, finally realizes the detection against network generated using hard exudates of fundus image pixel the level of DIARETDB1. The test is made on automatic detection algorithm proposed in fundus images of public data set, the sensitivity was 98.08%, specificity Sex 94.39%, positive predictive value 94.07% and accuracy rate 95.77%. Compared with other related research work, we can find that the method proposed in this paper has certain advantages and potential application value.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R587.2;R774.1;TP391.41

【參考文獻】

相關(guān)期刊論文 前5條

1 呂衛(wèi);翟慶偉;褚晶輝;李U，

本文編號：1535780