【摘要】：由于肝臟超聲圖像具有回聲不均勻、邊緣模糊等缺點,肝臟疾病的無創(chuàng)診斷易受影響,而且目前臨床基于肝臟超聲圖像的肝病診斷主要依靠醫(yī)生的主觀判斷,其缺點為依賴醫(yī)生主觀經(jīng)驗且耗時,因此提出一種基于局部二值模式(LBP)特征提取和稀疏表示的肝病識別算法。從肝臟超聲圖像中提取感興趣區(qū)域,使用LBP特征提取方法對感興趣區(qū)域提取圖像特征,將得到的特征進行字典訓(xùn)練,得到稀疏矩陣,最終采取支持向量機對其進行分類。實驗樣本均取自青島大學(xué)附屬醫(yī)院肝膽科。實驗1使用該方法對100個正常肝臟樣本和100個肝硬化樣本進行分類,準確率達到99.50%,實驗2使用該方法對肝硬化、脂肪肝、肝血管瘤和肝癌4類樣本共200個進行分類,AUC值分別為67.2%、65.1%、55.0%和62.6%。ROC曲線表明,提出的分類方法在準確率和泛化能力上均優(yōu)于傳統(tǒng)方法,有助于肝病的臨床診斷。
[Abstract]:Due to the shortcomings of non-invasive diagnosis of liver diseases, such as uneven echo and blurred edges, the diagnosis of liver diseases based on liver ultrasound images mainly depends on the subjective judgment of doctors. The shortcomings of the algorithm are that it depends on the subjective experience of doctors and is time-consuming. Therefore, a liver disease recognition algorithm based on local binary pattern (LBP) feature extraction and sparse representation is proposed. The region of interest is extracted from the liver ultrasound image, and the region of interest is extracted by using the LBP feature extraction method. The obtained features are trained in dictionary to obtain sparse matrix, and finally the support vector machine is used to classify the region of interest. All samples were taken from Department of Hepatobiliary, affiliated Hospital of Qingdao University. Experiment 1 used this method to classify 100 normal liver samples and 100 liver cirrhosis samples, and the accuracy was 99.500.Experiment 2 used this method to classify liver cirrhosis and fatty liver. A total of 200 samples of liver hemangioma and liver cancer were classified with AUC of 67.2% and 65.1%, respectively, and the 62.6%.ROC curve showed that the proposed classification method was superior to the traditional method in accuracy and generalization ability, and was helpful for the clinical diagnosis of liver diseases.
【作者單位】： 青島大學(xué)計算機科學(xué)技術(shù)學(xué)院;山東省數(shù)字醫(yī)學(xué)與計算機輔助手術(shù)重點實驗室;加州大學(xué)洛杉磯分校;
【基金】：國家自然科學(xué)基金(61303079;61305045)
【分類號】：R575;TP391.41

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