本文選題：圖像分割 + 圖像配準(zhǔn)　； 參考：《電子學(xué)報(bào)》2017年09期

【摘要】：配準(zhǔn)技術(shù)在基于多圖譜的分割方法中能有效地將醫(yī)學(xué)圖譜的先驗(yàn)知識(shí)融入分割過(guò)程,再結(jié)合以高效的標(biāo)記融合算法,最終實(shí)現(xiàn)精確地自動(dòng)分割.針對(duì)圖譜配準(zhǔn)的較大誤差及其對(duì)標(biāo)記融合的重要影響,本文建立了一種新的概率圖模型框架并以此提出了基于多參數(shù)配準(zhǔn)模型的分割算法,將此方法與高效的標(biāo)記融合算法相結(jié)合,可以提高目標(biāo)圖像中特定組織區(qū)域的分割精度,更使其在少量圖譜分割的情形下具有重要應(yīng)用.首先,使用多種配準(zhǔn)參數(shù)對(duì)所有目標(biāo)圖像進(jìn)行配準(zhǔn);然后,分別采用不同的算法對(duì)配準(zhǔn)圖像進(jìn)行灰度融合和標(biāo)記融合,實(shí)現(xiàn)訓(xùn)練圖像的重構(gòu)過(guò)程;最后,利用高效的標(biāo)記融合算法對(duì)重構(gòu)后的圖像進(jìn)行融合得到最終精確的分割結(jié)果.實(shí)驗(yàn)結(jié)果表明該方法均優(yōu)于本文其他分割算法,能夠有效提升腦部組織分割精度.
[Abstract]:Registration technology can effectively integrate the priori knowledge of medical atlas into the segmentation process in the multi-atlas based segmentation method, and then combine the efficient label fusion algorithm to achieve accurate automatic segmentation.In view of the large error of map registration and its important influence on marker fusion, a new probabilistic graph model framework is established and a segmentation algorithm based on multi-parameter registration model is proposed.The combination of this method and the efficient label fusion algorithm can improve the segmentation accuracy of the specific tissue areas in the target image, and make it more important in the case of a small number of map segmentation.First, we use a variety of registration parameters to register all the target images. Then, we use different algorithms to carry out gray level fusion and label fusion to realize the reconstruction process of the training image.An efficient label fusion algorithm is used to fuse the reconstructed images to obtain accurate segmentation results.The experimental results show that the proposed method is superior to other algorithms and can effectively improve the accuracy of brain tissue segmentation.
【作者單位】： 上海電力學(xué)院自動(dòng)化工程學(xué)院;國(guó)網(wǎng)浙江省電力公司金華供電公司;
【基金】：國(guó)家自然科學(xué)基金(No.61203224) 上海市教育委員會(huì)創(chuàng)新項(xiàng)目(No.13YZ101)
【分類(lèi)號(hào)】：R318;TP391.41
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本文編號(hào)：1752863