Segmentation of multiple sclerosis lesions in MR images: a review
Segmentation of multiple sclerosis lesions in MR images: a review
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DOI：
10.1007/s00234-011-0886-7
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发表时间：
2012-04-01
期刊：
影响因子：
2.8
通讯作者：
Soltanian-Zadeh, Hamid
中科院分区：
文献类型：
--
作者：
Mortazavi, Daryoush;Kouzani, Abbas Z.;Soltanian-Zadeh, Hamid
多发性硬化症（MS）是一种炎症性脱髓鞘疾病，部分神经系统通过病变在脑白质中产生。它会导致身体不同器官的残疾，如眼睛和肌肉。早期发现多发性硬化症和估计其进展是关键的最佳治疗的疾病。为了对MS病变进行诊断和治疗评估，它们可以在脑磁共振成像（MRI）扫描中被检测和分割。然而，由于需要分析的MRI数据量很大，临床专家手工分割病变是一项非常繁琐和耗时的任务。此外，人工分割是主观的，容易出现人为错误。几个小组已经开发了计算机方法来检测和分割多发性硬化症病变。这些方法在过去没有分类和比较。本文对近年来提出的各种多发性硬化症病灶分割方法进行了综述和比较。它涵盖了传统的方法，如多层阈值和区域增长，以及最近需要参数估计算法的贝叶斯方法。它还涵盖了参数估计方法，如期望最大化和自适应混合模型中的无监督技术，以及kNN和Parzen窗口方法中的监督技术。基于知识的方法（如基于地图集的方法）与贝叶斯方法的集成提高了分割的准确性。此外，采用模糊c均值、模糊推理系统和人工神经网络等智能分类器可以减少错误分类的体素。
Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease.For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past.This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques.Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.