Medical Image Segmentation: A Brief Survey
Medical Image Segmentation: A Brief Survey
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DOI：
10.1007/978-1-4419-8204-9_1
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发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
El-Baz, Ayman
中科院分区：
文献类型：
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作者：
Elnakib, Ahmed;Gimel'farb, Georgy;El-Baz, Ayman
准确分割2-D、3-D和4-D医学图像以分离感兴趣的解剖对象以进行分析，在几乎任何计算机辅助诊断系统或其他医学成像应用中都是必不可少的。多年来，许多出版物已经广泛地探讨了分割特征和算法的各个方面。然而，这一问题仍然具有挑战性，没有通用和唯一的解决方案，这是因为感兴趣的对象数量巨大且不断增加，它们在图像中的属性变化很大，不同的医学成像模式，以及每个对象的信号均质性、可变性和噪声的相关变化。本章概述了最流行的医学图像分割技术，并讨论了它们的性能、基本优势和局限性。还概述了过去十年最先进的技术。
Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical objects of interest for analysis is essential in almost any computer-aided diagnosis system or other medical imaging applications. Various aspects of segmentation features and algorithms have been extensively explored for many years in a host of publications. However, the problem remains challenging, with no general and unique solution, due to a large and constantly growing number of different objects of interest, large variations of their properties in images, different medical imaging modalities, and associated changes of signal homogeneity, variability, and noise for each object. This chapter overviews most popular medical image segmentation techniques and discusses their capabilities, and basic advantages and limitations. The state-of-the-art techniques of the last decade are also outlined.