• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on image processing >3D tensor-based blind multi-spectral image decomposition for tumor demarcation
【24h】

3D tensor-based blind multi-spectral image decomposition for tumor demarcation

机译：基于3D扭曲的盲盲多光谱图像分解对肿瘤分界

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).
机译：用于肿瘤划分的多光谱荧光图像的盲分解被配制利用图像的姿态结构。本文的首先贡献是识别来自Tucker3或3D图像张量的图像中存在的材料中存在的材料的光谱响应和3D张扭矩的矩阵。纸张的第二贡献是基于基于图像的估计存在于图像中存在的材料的数量以及它们的光谱分布的矩阵。通过多光谱图像张量的3模式乘法和频谱谱矩阵的三模式乘法回收材料的3D张量。多光谱图像的张量表示保留其局部空间结构，由于矢量化过程，当使用基于矩阵分解的分解方法（例如非负矩阵分解和独立分量分析）时，其丢失。在具有已知地面真理的实验红绿蓝（RGB）图像上对基于矩阵分解的分解的富集基图像分解的优异性能以及皮肤肿瘤的RGB荧光图像（基底细胞癌）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号