【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(xué)習(xí)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(yùn)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(fù)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(fù)樣本區(qū)域提取多種特征,把結(jié)構(gòu)紊亂作為目標(biāo)域的訓(xùn)練和測試集分別進(jìn)行特征提取,使用自適應(yīng)支持向量機(jī)(A-SVM)進(jìn)行分類。實(shí)驗(yàn)在乳腺鉬靶攝影數(shù)字化數(shù)據(jù)庫(DDSM)上進(jìn)行,實(shí)驗(yàn)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(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;武漢科技大學(xué)智能信息處理與實(shí)時工業(yè)系統(tǒng)湖北省重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金項(xiàng)目(61403287、61472293、31201121) 中國博士后科學(xué)基金項(xiàng)目(2014M552039) 湖北省自然科學(xué)基金項(xiàng)目(2014CFB288)
【分類號】：R737.9;TP391.41
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本文編號：2192244