【摘要】：中醫(yī)望診是一種傳統(tǒng)的疾病診斷方式,其中舌像診斷是很重要的一個組成部分。舌像診斷主要分為舌面診斷和舌下靜脈診斷等,對于舌面診斷已經(jīng)有了許多針對性的研究,而且經(jīng)過多年的發(fā)展已經(jīng)產(chǎn)生了許多成果,但是關于舌下靜脈的研究還比較的少,有待進一步的探討。本課題主要是在舌下靜脈的影像信息中,尋找對于疾病分析有潛在價值、有指導意義的特征,并根據(jù)這些特征來進行健康與疾病的分析。具體研究內(nèi)容包括:圖像的采集與預處理、舌下靜脈血管的分割、特征選擇與優(yōu)化以及基于舌下靜脈血管特征的聚類分析。通過與醫(yī)院合作,采集到舌下靜脈圖像的樣本,同時得到對應樣本的各類生化指標以及醫(yī)生所做出的健康狀況判定結果,并把這些信息作為該樣本的疾病標簽。截止目前已采集含標簽樣本兩千多例。本課題選取其中采集樣本較多的類別,如肺癌、高血壓、乳腺癌、腎病、失眠、糖尿病、胃炎、腫瘤以及健康樣本等作為數(shù)據(jù)集。對于采集到的原始圖像采用一種基于多項式的校正算法進行顏色校正,并提出了一種基于HSI與LUV顏色空間像素生長法的交互式分割算法進行舌下靜脈圖像的分割�；�1000幅分割后的舌下靜脈圖像構建了舌下靜脈的顏色空間,并通過k-means聚類方法獲得了舌下靜脈基于該顏色空間的顏色特征;同時,根據(jù)分割的舌下靜脈提取了基于RGB和HSV顏色空間下的顏色特征,以及舌下靜脈長度、寬度以及長寬比等幾何特征,并通過不同的特征組合進行了特征向量的優(yōu)化。利用舌下靜脈特征向量對采集的圖像樣本進行了聚類分析,主要進行了健康與幾種疾病的二分類,通過不同的二分類算法的比較選擇了SVM分類器算法進行二分類,最后分類結果的平均正確度為80.88%,而對于幾種典型的疾病如2型糖尿病與健康的分類精度可以達到89.34%,這說明舌下靜脈這一特征對于疾病分析確實有意義。實現(xiàn)了SVM決策樹的多分類器,對于健康、失眠和乳腺癌三類的分類準確度可以達到70.19%,說明SVM決策樹算法對于多分類問題可以有比較好的效果,并且證明舌下靜脈對于疾病的診斷分析是有意義的。
[Abstract]:Traditional Chinese medicine diagnosis is a traditional way of disease diagnosis, among which tongue image diagnosis is an important part. Tongue image diagnosis is mainly divided into tongue surface diagnosis and sublingual vein diagnosis. There have been many targeted studies on tongue surface diagnosis, and after years of development, many achievements have been produced, but the research on sublingual vein is still relatively few. Further discussion is needed. In this paper, we mainly look for the potential and instructive features of disease analysis in the image information of sublingual vein, and analyze the health and disease according to these characteristics. The specific research contents include: image acquisition and preprocessing, sublingual vein segmentation, feature selection and optimization, and clustering analysis based on sublingual vein features. By cooperating with the hospital, the samples of the sublingual vein images were collected, and the biochemical indexes of the corresponding samples and the results of the doctor's health assessment were obtained, and the information was used as the disease label of the sample. Up to now, more than 2,000 samples containing labels have been collected. In this study, we selected a large number of samples, such as lung cancer, hypertension, breast cancer, nephropathy, insomnia, diabetes, gastritis, tumor and health samples as data sets. A polynomial correction algorithm is used to correct the original image and an interactive segmentation algorithm based on HSI and LUV color space pixel growth method is proposed to segment the sublingual vein image. The color space of the sublingual vein is constructed based on 1000 segmented images of the sublingual vein, and the color feature of the sublingual vein based on the color space is obtained by k-means clustering. Based on the segmented sublingual veins, the color features based on RGB and HSV color spaces, as well as the geometric features such as the length, width and aspect ratio of the sublingual veins are extracted, and the feature vectors are optimized by different feature combinations. The sublingual vein feature vector is used to cluster the collected image samples. The two classification methods of health and several diseases are mainly carried out. The SVM classifier algorithm is selected to do the two classification by comparing different two classification algorithms. The average accuracy of the classification results is 80.88, while the classification accuracy of several typical diseases such as type 2 diabetes and health can reach 89.34, which indicates that the sublingual vein is of great significance for disease analysis. The classification accuracy of SVM decision tree can reach 70.19 for health, insomnia and breast cancer. It shows that the SVM decision tree algorithm has good effect on multi-classification problem. It also proves that the sublingual vein is of significance in the diagnosis and analysis of the disease.
【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】：2016
【分類號】：R241;TP391.41

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