【摘要】：乳腺X線圖像腫塊的良惡性分類是計(jì)算機(jī)輔助診斷的研究?jī)?nèi)容之一,本文對(duì)乳腺X線圖像腫塊邊緣分割及不同特征的腫塊良惡性分類進(jìn)行研究.基于最大化分割后圖像類間方差的思想,提出了一種改進(jìn)控制標(biāo)記分水嶺方法完成粗分割,然后采用無邊緣活動(dòng)輪廓(CV)模型對(duì)粗分割結(jié)果進(jìn)行修正.為了驗(yàn)證不同特征在腫塊良惡性分類中的性能,對(duì)現(xiàn)有形狀特征、紋理特征在不同分類器下的分類性能進(jìn)行測(cè)試.實(shí)驗(yàn)在開源數(shù)據(jù)庫DDSM上驗(yàn)證,結(jié)果表明,在通過自動(dòng)分割方法得到腫塊邊緣的情況下,紋理特征的分類性能更好.
[Abstract]:The classification of benign and malignant breast X-ray masses is one of the research contents of computer aided diagnosis. In this paper, the edge segmentation of breast X-ray masses and the classification of benign and malignant masses with different characteristics were studied. Based on the idea of maximizing the inter-class variance of images after segmentation, an improved control mark watershed method is proposed to complete rough segmentation, and then the rough segmentation results are modified by (CV) model without edge active contours. In order to verify the performance of different features in the classification of benign and malignant masses, the classification performance of existing shape features and texture features under different classifiers was tested. The experiment is verified on the open source database DDSM. The results show that the classification performance of texture features is better when the edge of the mass is obtained by automatic segmentation.
【作者單位】： 北京交通大學(xué)電子信息工程學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61502025,61571036) 中國博士后科學(xué)基金項(xiàng)目(2015M570029) 北京交通大學(xué)人才基金(2015RC024) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(2016JBM010)~~
【分類號(hào)】：R730.44;R737.9;TP391.41

【相似文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 顧曉雯;單、雙指數(shù)擴(kuò)散加權(quán)成像在肺結(jié)節(jié)/腫塊良惡性鑒別中的初步探討[D];南通大學(xué);2016年

2 侯慧;基于分水嶺算法的白細(xì)胞分割研究[D];湘潭大學(xué);2016年

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本文編號(hào)：2488621