国产bbaaaaa片,成年美女黄网站色视频免费,成年黄大片,а天堂中文最新一区二区三区,成人精品视频一区二区三区尤物

首頁> 美國衛(wèi)生研究院文獻>other >From label fusion to correspondence fusion: a new approach to unbiased groupwise registration
【2h】

From label fusion to correspondence fusion: a new approach to unbiased groupwise registration

機譯：從標簽融合通信的融合：一種新的方法以公正的GroupWise注冊

代理獲取
本網(wǎng)站僅為用戶提供外文OA文獻查詢和代理獲取服務，本網(wǎng)站沒有原文。下單后我們將采用程序或人工為您竭誠獲取高質(zhì)量的原文，但由于OA文獻來源多樣且變更頻繁，仍可能出現(xiàn)獲取不到、文獻不完整或與標題不符等情況，如果獲取不到我們將提供退款服務。請知悉。
  • 摘要
  • 著錄項
  • 相似文獻
  • 相關(guān)主題

摘要

Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [, , ]. Effective label fusion strategies, such as local similarity-weighted voting [, ] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate than those based on registration to a population template. In a 2D example in real MRI data, fusion methods result in more consistent mappings between manual segmentations of the hippocampus.

著錄項

相似文獻

  • 外文文獻
  • 中文文獻
  • 專利
代理獲取

客服郵箱：kefu@zhangqiaokeyan.com

京公網(wǎng)安備：11010802029741號 ICP備案號：京ICP備15016152號-6 六維聯(lián)合信息科技 (北京) 有限公司?版權(quán)所有

  • 客服微信

  • 服務號