北京生物醫(yī)學(xué)工程

基于特征融合的肝包蟲病CT圖像識別

CT image recognition of liver hydatid disease based on feature fusion

作者：排孜麗耶·尤山塔依嚴(yán)傳波木拉提·哈米提姚娟阿布都艾尼·庫吐魯克吳淼

單位：新疆醫(yī)科大學(xué)基礎(chǔ)醫(yī)學(xué)院（烏魯木齊 830011）新疆醫(yī)科大學(xué)醫(yī)學(xué)工程技術(shù)學(xué)院（烏魯木齊 830011）新疆醫(yī)科大學(xué)第一附屬醫(yī)院影像中心（烏魯木齊 830011）

關(guān)鍵詞：肝包蟲病；特征融合；計算機輔助診斷；特征提取；分類識別

分類號：R318.04

出版年·卷·期（頁碼）：2019·38·4（400-406）

摘要：

目的探討特征融合方法在肝包蟲病CT圖像分類識別中的應(yīng)用，旨在提高肝包蟲病的診斷準(zhǔn)確率。方法選取正常肝臟和單囊型肝包蟲病CT圖像各150張，對每幅圖像采取空域與頻域濾波算法、數(shù)學(xué)形態(tài)學(xué)算法和點處理，分別得到10幅特征子圖像并對它們進(jìn)行特征融合，對融合后的圖像提取灰度和紋理特征，通過統(tǒng)計學(xué)分析篩選關(guān)鍵特征。結(jié)果對提取的10維特征進(jìn)行統(tǒng)計學(xué)分析，得到正常肝臟和單囊型肝包蟲CT融合圖像之間完全沒有交集的4個灰度和1個紋理特征取值范圍，由此來區(qū)分肝包蟲病與正常肝臟CT圖像。結(jié)論從原始圖像中提取特征子圖像并進(jìn)行融合，再對融合后圖像提取特征的方法能夠很好地區(qū)分識別正常肝臟和單囊型肝包蟲病CT圖像，為肝包蟲病的早期診斷提供依據(jù)。

Objective To discuss the application of feature fusion method in the CT image classification and recognition of hepatic hydatid disease, in order to improve the diagnosis classification of hepatic echinococcosis. Methods CT medical images of normal liver and single cystic hepatic hydatid disease were selected，each 150 spatial and frequency-domain filtering algorithms, mathematical morphology algorithms and point processing were used to each image to obtain 10 feature images respectively and fused them effectively. Grayscale and texture features were extracted from each fused images, and the key features were selected by statistical analysis. Results Statistical analysis was performed on the extracted 10-dimensional features and obtained 4 gray scales and 1 texture features range with no intersection between the normal liver and single-cystic hepatic hydatid fused image, thereby distinguishing the liver hydatid disease and normal liver CT images. Conclusions Extracting feature sub-images from the original image and fusing them, extracting gray and texture features from the fused image can distinguish the CT images of normal liver and single-cystic hepatic hydatid, it also providing evidence for the early diagnosis of hepatic hydatid disease.

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