本文關鍵詞： 新疆高發(fā)病 肝棘球蚴病 CT圖像 特征提取 疾病分類　出處：《新疆醫(yī)科大學》2013年碩士論文　論文類型：學位論文

【摘要】：目的：對新疆高發(fā)病肝包蟲病CT圖像進行特征提取與特征分析，選擇具有較強分類能力的特征，進一步探討該特征在肝包蟲病圖像分類中的應用，為基于內容的新疆高發(fā)病肝包蟲病醫(yī)學圖像的檢索系統(tǒng)奠定基礎。方法：使用Matlab圖像處理軟件，對CT圖像進行預處理，改善圖像的質量，，保存有效信息，刪除無用信息；進而對處理后圖像提取基于灰度直方圖、灰度共生矩陣和柯爾莫戈洛夫復雜性的特征。使用SPSS統(tǒng)計分析軟件，對圖像特征進行最大類間距法分析和顯著性分析，并且根據分析結果組成圖像的綜合特征；進一步使用分析所得特征對新疆高發(fā)病肝包蟲病CT圖像分類。結果：對新疆高發(fā)病肝包蟲病CT圖像灰度直方圖、灰度共生矩陣和柯爾莫戈洛夫復雜性特征，使用最大類間距方法分析，結果顯示，用灰度直方圖特征、灰度共生矩陣特征和綜合特征分類中，分類正常肝臟圖像和單囊型肝包蟲病圖像時，分類準確率分別是81%和71%，85%和66%，91%和87%；分類正常肝臟圖像和多囊型肝包蟲病圖像時，分類準確率分別為89%和82%，81%和72%，90%和93%；分類單囊型肝包蟲病圖像和多囊型肝包蟲病圖像時，分類準確率分別是75%和74%，75%和76%，85%和80%。對圖像灰度直方圖、灰度共生矩陣和柯爾莫戈洛夫復雜性特征，使用顯著性方法分析，結果顯示，用灰度直方圖特征、灰度共生矩陣特征和綜合特征分類正常肝臟圖像、單囊型肝包蟲病圖像和多囊型肝包蟲病圖像時，分類準確率分別為84%、58%和77%，82%、77%和87%，96%、86%和86%。結論：將圖像特征提取方法成功引入新疆高發(fā)病肝包蟲病CT圖像的分析中，對肝包蟲病CT圖像進行特征提取和特征分析并生成圖像的綜合特征，該特征對新疆高發(fā)病肝包蟲病CT圖像的分類準確率相對單一特征較高，在一定程度上滿足分類需求，且特征分析結果可以進一步應用到基于內容的新疆高發(fā)病肝包蟲病醫(yī)學圖像檢索系統(tǒng)中，具有一定的應用價值。
[Abstract]:Objective: to extract and analyze the CT features of high incidence liver hydatid disease in Xinjiang, select the feature with strong classification ability, and discuss the application of this feature in the classification of liver hydatid disease image. Methods: Matlab image processing software was used to preprocess CT images, improve the quality of images, save effective information and delete useless information. Then, the features based on gray histogram, gray level co-occurrence matrix and Colmogorov complexity are extracted from the processed image. Using SPSS statistical analysis software, the maximum class spacing method and significance analysis are used to analyze the image features. According to the analysis results, the comprehensive features of the images are formed, and the CT image classification of high incidence liver hydatid disease in Xinjiang is further used. Results: the gray histogram of CT image of high incidence liver hydatid disease in Xinjiang is analyzed. Grey level co-occurrence matrix and Colmogorov complexity feature are analyzed by the method of maximum class spacing. The results show that, in the classification of gray histogram feature, gray level co-occurrence matrix feature and synthesis feature, When classifying normal liver images and single-cystic liver hydatidosis images, the classification accuracy was 81% and 71%, respectively, and 66% and 91% and 87%, respectively, while classifying normal liver images and polycystic hepatic hydatidosis images, The classification accuracy rates were 89% and 821% and 722% and 93%, respectively. The classification accuracy was 75% and 7475% for monocystic liver hydatidosis images and 76,85% and 80% for polycystic liver hydatidosis images, respectively. Gray level co-occurrence matrix and Colmogorov complex feature were analyzed by using significant method. The results showed that normal liver images were classified by gray histogram feature, gray level co-occurrence matrix feature and comprehensive feature. The classification accuracy of monocystic hepatic hydatidosis and polycystic hepatic hydatidosis was 84% and 77 82%, respectively. Conclusion: the method of image feature extraction was successfully introduced to the analysis of CT images of high incidence liver hydatid disease in Xinjiang. The CT image of liver hydatid disease was extracted and analyzed and the comprehensive features of the image were generated. The classification accuracy of the CT image of liver hydatid disease in Xinjiang was relatively higher than that of the single feature, which met the classification needs to some extent. The results of feature analysis can be further applied to the medical image retrieval system of high incidence liver hydatid disease in Xinjiang based on content, which has certain application value.
【學位授予單位】：新疆醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.41;R532.32

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本文編號：1508920