本文關鍵詞： 視網膜微動脈瘤 主成分分析 核主成分分析 支持向量機　出處：《東北大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著計算機技術的發(fā)展,數字視網膜圖像處理與分析技術也日漸成熟。糖尿病性視網膜病變是一種嚴重的糖尿病并發(fā)癥,是導致新發(fā)失明的主要原因。目前,在糖尿病的診療過程中面臨的重大難題是如何安全高效地進行數字視網膜圖像篩查,及時地識別糖尿病性視網膜病變以便盡早地采取相應措施避免失明。本文主要針對糖尿病性視網膜病變中最早出現(xiàn)的微動脈瘤病征進行研究,提出了一種基于核主成分分析(KPCA)和支持向量機(SVM)的微動脈瘤檢測算法。根據微動脈瘤的檢測原理可知,視網膜微動脈瘤的檢測過程主要分為三大部分：微動脈瘤的候選集獲取、微動脈瘤特征抽取以及微動脈瘤特征分類。本文基于圖像預處理技術和微動脈瘤的粗提取理論獲取了視網膜微動脈瘤候選集,并深入研究了視網膜微動脈瘤的特征抽取算法和特征分類算法。本文首先研究了基于PCA的微動脈瘤特征抽取算法,該算法的實質是通過線性映射將微動脈瘤的特征樣本從高維的特征空間變換到較低維的特征子空間的過程。然而,由于PCA算法本身的局限性,在進行變換的過程中忽略了高階特征指標之間的相互關系,使降維后的主成分并不能很好地表達原來的高維特征指標所攜帶的信息。為解決該不足,本文提出了基于KPCA的微動脈瘤特征抽取算法,該算法首先通過核函數將高維特征向量映射到核空間,然后再在核空間上進行PCA處理,以實現(xiàn)高效地特征抽取。另外,本文在主成分分析的基礎上借助于截斷誤差法設計相應的分類器,實現(xiàn)了對低維微動脈瘤候選集的分類。實驗證明,雖然兩種算法均能達到特征抽取的目的,但是基于KPCA的特征抽取算法更能有效地降低特征維數,提高微動脈瘤的檢測真陽性率。其次,本文設計使用基于SVM的特征分類算法對特征抽取后的低維微動脈瘤候選集進行分類。針對傳統(tǒng)算法中單純采用各個特征量區(qū)間約束策略時忽視各個特征量的約束力大小的弊端,本文采用了基于統(tǒng)計學理論基礎的SVM分類算法,算法中關于核函數參數與懲罰因子的確定,本文采用粒子群優(yōu)化算法選取最優(yōu)的參數值。實驗證明,基于SVM特征分類算法能夠有效的降低微動脈瘤的檢測假陽性率。實驗結果顯示,綜合使用KPCA和SVM算法能有效提高微動脈瘤檢測算法的檢測精度。
[Abstract]:With the development of computer technology, digital retinal image processing and analysis technology is becoming more and more mature. Diabetic retinopathy is a serious complication of diabetes, which is the main cause of new blindness. In the course of diagnosis and treatment of diabetes mellitus, the major problem is how to screen digital retinal image safely and efficiently. In order to identify diabetic retinopathy in time and take appropriate measures to avoid blindness as early as possible, this paper mainly focuses on the microaneurysm symptoms of diabetic retinopathy. A microaneurysm detection algorithm based on kernel principal component analysis (KPCA) and support vector machine (SVM) was proposed. The detection of retinal microaneurysms is mainly divided into three parts: the acquisition of candidate sets of microaneurysms. Microaneurysm feature extraction and microaneurysm classification. Based on image preprocessing technique and rough extraction theory of microaneurysm, the candidate set of retinal microaneurysm was obtained in this paper. The feature extraction algorithm and feature classification algorithm of retinal microaneurysms are studied in depth. Firstly, the feature extraction algorithm of microaneurysms based on PCA is studied in this paper. The essence of the algorithm is to transform the feature samples of microaneurysms from the high-dimensional feature space to the lower-dimensional feature subspace by linear mapping. However, due to the limitations of the PCA algorithm itself. In the process of transformation, the relationship between high-order feature indexes is neglected, so that the reduced principal component can not express the information carried by the original high-dimensional feature index. In this paper, a feature extraction algorithm for microaneurysms based on KPCA is proposed. Firstly, high dimensional feature vectors are mapped to kernel space by kernel function, then PCA is processed in kernel space. In order to achieve efficient feature extraction. In addition, based on principal component analysis (PCA), the corresponding classifier is designed based on the truncation error method to realize the classification of low-dimensional microaneurysm candidate sets. Although the two algorithms can achieve the purpose of feature extraction, the feature extraction algorithm based on KPCA can effectively reduce the feature dimension and improve the true positive rate of microaneurysm detection. In this paper, the feature classification algorithm based on SVM is designed to classify the low-dimensional microaneurysm candidate set after feature extraction. The drawbacks of binding size. In this paper, SVM classification algorithm based on statistics theory is adopted. In the algorithm, the parameters of kernel function and penalty factor are determined, and the particle swarm optimization algorithm is used to select the optimal parameter value. Based on SVM feature classification algorithm can effectively reduce the false positive rate of microaneurysm detection. Experimental results show that the combined use of KPCA and SVM algorithm can effectively improve the detection accuracy of microaneurysm detection algorithm.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：R587.2;R774.1;TP391.41

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