Magnetic resonance imaging (MRI) is widely used in the medical field, especially for detecting serious abnormalities affecting the organs of the human body, such as tumors. Automatic detection of tumors needs high‐performance recognition techniques. In this paper, we have developed a new automatic method based on the multisegmentation of brain tumor region. We used an improved region‐growing algorithm, which is based on quasi‐Monte Carlo and expectation maximization methods to define the desired classes. Several metrics were calculated to evaluate the performance of our technique. The fully automatic multisegmentation approach, developed in this study, showed good performance, and it can offer a new option to replace conventional techniques used for tumor detection in MRI images.

中文翻译：

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使用改进的区域生长和准蒙特卡罗期望最大化算法的磁共振成像脑肿瘤检测全自动多分割方法

磁共振成像（MRI）广泛应用于医学领域，特别是用于检测影响人体器官的严重异常，如肿瘤。肿瘤的自动检测需要高性能的识别技术。在本文中，我们开发了一种新的基于脑肿瘤区域多分割的自动方法。我们使用了一种改进的区域增长算法，该算法基于准蒙特卡罗和期望最大化方法来定义所需的类。计算了几个指标来评估我们技术的性能。在这项研究中开发的全自动多分割方法表现出良好的性能，它可以提供一种新的选择来取代用于 MRI 图像中肿瘤检测的传统技术。