本文選題：虛擬光學(xué)密度圖像 + 乳房X線攝影術(shù)　； 參考：《中國(guó)醫(yī)學(xué)影像技術(shù)》2017年08期

【摘要】：目的探討對(duì)原始乳腺鉬靶圖像進(jìn)行變換和采用機(jī)器學(xué)習(xí)算法融合不同類型的圖像特征,以提高乳腺癌近期發(fā)病風(fēng)險(xiǎn)預(yù)測(cè)精度的價(jià)值。方法自匹茲堡大學(xué)醫(yī)學(xué)中心的臨床數(shù)據(jù)庫(kù)下載185例女性受檢者頭足(CC)位全數(shù)字化乳腺X線攝影(FFDM)圖像。首先對(duì)原始灰度圖像進(jìn)行乳腺區(qū)域分割并將其變換為虛擬光學(xué)密度圖像,而后從原始灰度圖像和虛擬光學(xué)密度圖像中分別提取不對(duì)稱特征。基于此不對(duì)稱特征分別訓(xùn)練第1階段的2個(gè)決策樹(shù)分類器,再以這2個(gè)分類器輸出的得分值作為輸入,訓(xùn)練第2階段的1個(gè)決策樹(shù)分類器。對(duì)乳腺癌近期發(fā)病風(fēng)險(xiǎn)預(yù)測(cè)性能采用留一法進(jìn)行驗(yàn)證。結(jié)果采用兩階段決策樹(shù)融合方法預(yù)測(cè)乳腺癌的ROC曲線下面積(AUC)為0.9612±0.0132,敏感度為96.63%(86/89),特異度為91.67%(88/96),準(zhǔn)確率為94.05%(174/185)。結(jié)論從虛擬光學(xué)密度圖像中可提取出對(duì)乳腺癌具有較高預(yù)測(cè)力的特征,采用兩階段決策樹(shù)方法對(duì)兩類圖像特征進(jìn)行二次融合有助于提高乳腺癌近期發(fā)病風(fēng)險(xiǎn)預(yù)測(cè)精度。
[Abstract]:Objective to improve the accuracy of breast cancer risk prediction by transforming original mammary mammography and using machine learning algorithm to fuse different types of image features. Methods A total digital mammography (FFDM) image of 185 female subjects was downloaded from the clinical database of University of Pittsburgh Medical Center. Firstly, the original gray image is segmented and transformed into a virtual optical density image, and then the asymmetric features are extracted from the original gray image and the virtual optical density image. Based on this asymmetric feature, two decision tree classifiers in the first stage are trained, and the score values of the two classifiers are used as input to train a decision tree classifier in the second stage. The predictive performance of breast cancer risk in the near future was verified by one-left-one method. Results the ROC curve area under the ROC curve was 0.9612 鹵0.0132, the sensitivity was 96.63 / 86 / 89, the specificity was 91.67 / 96 / 96, and the accuracy was 94.05 / 1754 / 185.Results by using the two-stage decision tree fusion method, the area under the ROC curve was 0.9612 鹵0.0132, the sensitivity was 96.63 / 86 / 89, the specificity was 91.67 / 96. Conclusion the features with high predictive power for breast cancer can be extracted from the virtual optical density image. Using the two-stage decision tree method to perform secondary fusion of the two kinds of image features is helpful to improve the prediction accuracy of the risk of breast cancer in the near future.
【作者單位】： 上海理工大學(xué)醫(yī)療器械與食品學(xué)院;
【分類號(hào)】：R730.44;R737.9
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本文編號(hào)：1934192