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基于动态规划的生物医学成像分割

Dynamic Programming Based Segmentation in Biomedical Imaging.

作者信息

Ungru Kathrin, Jiang Xiaoyi

机构信息

Department of Mathematics and Computer Science, University of Münster, Münster, Germany.

Department of Mathematics and Computer Science, University of Münster, Münster, Germany; Cluster of Excellence EXC 1003, Cells in Motion, Münster, Germany.

出版信息

Comput Struct Biotechnol J. 2017 Feb 16;15:255-264. doi: 10.1016/j.csbj.2017.02.001. eCollection 2017.

Abstract

Many applications in biomedical imaging have a demand on automatic detection of lines, contours, or boundaries of bones, organs, vessels, and cells. Aim is to support expert decisions in interactive applications or to include it as part of a processing pipeline for automatic image analysis. Biomedical images often suffer from noisy data and fuzzy edges. Therefore, there is a need for robust methods for contour and line detection. Dynamic programming is a popular technique that satisfies these requirements in many ways. This work gives a brief overview over approaches and applications that utilize dynamic programming to solve problems in the challenging field of biomedical imaging.

摘要

生物医学成像中的许多应用都需要自动检测骨骼、器官、血管和细胞的线条、轮廓或边界。目的是在交互式应用中辅助专家决策,或将其作为自动图像分析处理流程的一部分。生物医学图像常常存在噪声数据和模糊边缘的问题。因此,需要有强大的轮廓和线条检测方法。动态规划是一种在很多方面都能满足这些要求的常用技术。本文简要概述了利用动态规划解决生物医学成像这一具有挑战性领域中的问题的方法及应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e1d/5338725/78bc72c80e77/fx1.jpg

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