排孜麗耶·尤山塔依 嚴傳波 木拉提·哈米提
摘 要:目的 探討計算機輔助診斷技術在肝包蟲病和肝囊腫CT圖像分型中的應用。方法 對單囊型肝包蟲病和單發性肝囊腫CT圖像感興趣區域,分別使用傳統的預處理方法和圖像融合方法,提取原始ROI、預處理后的和融合后的ROI圖像Haar小波、DB2小波、Tamura、Gabor濾波器和灰度-梯度共生矩陣特征,通過支持向量機和BP神經網絡分類模型分類,比較三種方法的分類準確率,并對各分類模型進行參數評估。結果 從原始ROI圖像直接提取的Haar小波、DB2小波、Tamura和GGCM特征的最佳分類準確率均達到了95%以上;融合后的ROI圖像五種特征的分類準確率都較高,在90%以上。結論 本研究所使用的方法應用于肝包蟲病和肝囊腫CT圖像的分型中具有一定的分類優勢,為影像學診斷提供依據。
關鍵詞:肝包蟲病;肝囊腫;圖像融合;特征提取;圖像分類
中圖分類號:R532.32;R575.4 文獻標識碼:A DOI:10.3969/j.issn.1006-1959.2018.23.018
文章編號:1006-1959(2018)23-0061-06
Abstract:Objective To discuss the application of computer aided diagnosis in classification of hepatic hydatid disease and hepatic cyst CT images.Methods For the region of the CT image of single cystic hepatic echinococcosis and single hepatic cyst, the original ROI, pre-processed and fused ROI image Haar wavelet, DB2 were extracted using traditional pre-processing methods and image fusion methods, respectively. Wavelet, Tamura, Gabor filter and gray-gradient co-occurrence matrix characteristics are classified by support vector machine and BP neural network classification model. The classification accuracy of the three methods is compared, and the parameters of each classification model are evaluated. Results The best classification accuracy of Haar wavelet, DB2 wavelet, Tamura and GGCM features extracted from the original ROI image reached more than 95%. The classification accuracy of the five characteristics of the ROI image after fusion is higher,above 90%.Conclusion The method used in this study has a certain classification advantage in the classification of CT images of hepatic hydatidosis and hepatic cysts, and provides a basis for imaging diagnosis.
Key words:Hepatic echinococcosis;Hepatic cyst;Image fusion;Feature extraction; Image classification
肝包蟲病(hepatic echinococcosis)是一種人畜共患的寄生蟲病,由棘球絳幼蟲侵染人體肝臟所致,又名肝棘球蚴病,從病理上分為肝細粒棘球蚴病(也稱肝包蟲囊腫)和肝泡狀棘球蚴病(也叫肝的泡狀包蟲病)兩種,肝包蟲囊腫較多見[1]。肝包蟲病的發病具有地域性特點,多發于牧區,在我國畜牧業發達的新疆、西藏、內蒙、寧夏等省區較常見,當地有著“蟲癌”之稱。肝囊腫是常見的肝臟良性病變,又稱非寄生蟲性肝囊腫,大部分是一種先天性疾病[2]。肝包蟲病和肝囊腫均起病隱匿,潛伏期較長,初期癥狀不明顯,臨床表現相似,易誤診[3]。……