【摘要】：針對乳腺X圖像中結(jié)構(gòu)紊亂識別困難、樣本數(shù)量較少的問題,提出基于遷移學習的結(jié)構(gòu)紊亂識別方法,把基于Gabor的毛刺模式特征、GLCM特征以及熵特征等新特征運用其中�；趷盒阅[塊與結(jié)構(gòu)紊亂的相似性,把惡性腫塊作為源域中正樣本,負樣本由結(jié)構(gòu)紊亂檢測算法中的偽正樣本構(gòu)成,對正負樣本區(qū)域提取多種特征,把結(jié)構(gòu)紊亂作為目標域的訓練和測試集分別進行特征提取,使用自適應(yīng)支持向量機(A-SVM)進行分類。實驗在乳腺鉬靶攝影數(shù)字化數(shù)據(jù)庫(DDSM)上進行,實驗結(jié)果表明,該方法克服了結(jié)構(gòu)紊亂樣本數(shù)量少的問題,提高了結(jié)構(gòu)紊亂的識別率。
[Abstract]:In view of the difficulty of structural disorder recognition and the small number of samples in mammogram, a new method of structural disorder recognition based on transfer learning is proposed, in which the new features such as Gabor burr pattern feature and entropy feature are used. Based on the similarity between malignant mass and structural disorder, the malignant mass is regarded as a positive sample in the source domain. The negative sample is composed of pseudo positive samples in the structural disorder detection algorithm, and a variety of features are extracted from the positive and negative sample regions. The training and test sets of structure disorder as the target domain are used for feature extraction, and adaptive support vector machine (A-SVM) is used for classification. The experiment was carried out on the digital mammography database (DDSM). The experimental results show that the method overcomes the problem of small number of structural disorder samples and improves the recognition rate of structural disorder.
【作者單位】： 武漢科技大學計算機科學與技術(shù)學院;武漢科技大學智能信息處理與實時工業(yè)系統(tǒng)湖北省重點實驗室;
【基金】：國家自然科學基金項目(61403287、61472293、31201121) 中國博士后科學基金項目(2014M552039) 湖北省自然科學基金項目(2014CFB288)
【分類號】：R737.9;TP391.41
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本文編號：2192244