本文選題：乳腺磁共振圖像 + 病灶分割��； 參考：《天津大學(xué)學(xué)報(bào)(自然科學(xué)與工程技術(shù)版)》2017年08期

【摘要】：針對乳腺磁共振圖像序列的腫瘤分割問題,提出一種基于超像素和改進(jìn)C-V模型的三維全自動分割方法.該方法利用磁共振圖像序列的幀間相關(guān)性,約束相鄰幀圖像的分割輪廓.采用超像素算法提取腫瘤的大致輪廓,再用改進(jìn)的C-V水平集算法對可疑區(qū)域邊緣進(jìn)行優(yōu)化,使其更接近腫瘤的實(shí)際邊緣.將該方法及3種對比方法應(yīng)用于89例乳腺M(fèi)RI序列圖像.以手動分割的輪廓為基準(zhǔn),該方法得到的平均重疊率為87.84%,,相比于C-V模型的58.90%,、超像素和水平集結(jié)合的76.36%,、K均值+C-V的83.62%,,有明顯提升.實(shí)驗(yàn)結(jié)果表明,該方法的全自動分割結(jié)果對于腫瘤起始和終止幀圖像具有較高的分割精度.
[Abstract]:A three dimensional fully automatic segmentation method based on super pixel and improved C-V model is proposed for tumor segmentation in the breast MRI sequence. This method uses the interframe correlation of the MRI image sequence to restrict the segmentation contour of the adjacent frame images. The rough contour of the tumor is extracted with the super pixel algorithm, and the improved C-V level set is used. The algorithm optimizes the edge of the suspicious area to make it closer to the actual edge of the tumor. This method and 3 contrast methods are applied to 89 cases of breast MRI sequences. The average overlap rate of this method is 87.84%, compared with 58.90% of the C-V model, 76.36% of the combination of super pixels and the level set, and the K mean +C- The experimental results show that the automatic segmentation results of V have a higher segmentation accuracy for tumor initiation and termination frame images. 83.62%.
【作者單位】： 天津大學(xué)電氣自動化與信息工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61271069)~~
【分類號】：R737.9;TP391.41
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本文編號：2042482