In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation; manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter- and intra-expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated.

中文翻译：

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多发性硬化症分割和分类的先进计算方法综述

在这项研究中，提出了基于磁共振成像的多发性硬化症（MS）分类和分割过程的调查。通过确定样本病变的数量来获得MS病变的知识，以便可以精确地跟踪病变的发展水平。因此，可以准确评估药物在医学测试中的作用。在磁共振图像中准确识别MS病变是另外一个复杂的过程，因为它们的形状和大小会发生变化，而这些形状和大小可能很难根据各种对象的解剖位置进行识别。这可以通过精确细分来确定；手动分割将非常难以执行，因为它需要高级知识，这需要花费更多时间。为了执行病变的自动分割，需要确定专家之间和专家内部的变异性。这项调查工作的主要目的是提供对不同分类和细分方法及其技术的分析。通过考虑并仔细评估过去的工作，这项调查工作对于从事MS的研究人员将是有价值的。审查了现有技术的优缺点，并阐明了MS病变分割和分类的问题。