Bagheri Rouholla, Monfared Jalal Haghighat, Montazeriyoun Mohammad Reza
Department of Management, Ferdowsi University of Mashhad, Iran.
Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
J Med Signals Sens. 2021 Oct 20;11(4):285-290. doi: 10.4103/jmss.JMSS_43_20. eCollection 2021 Oct-Dec.
It is important to have an accurate and reliable brain tumor segmentation for cancer diagnosis and treatment planning. There are few unsupervised approaches for brain tumor segmentation. In this paper, a new unsupervised approach based on graph coloring for brain tumor segmentation is introduced. In this study, a graph coloring approach is used for brain tumor segmentation. For this aim, each pixel of brain image assumed as a node of graph and difference between brightness of a couple of pixels considered as edge. This method was applied on T1-enhanced magnetic resonance images of low-grade and high-grade patients. Since a rigid graph was needed for graph coloring, edges must be divided into existing or nonexisting edge using a threshold. The value of this threshold has affected the accuracy of image segmentation, so the choice of the optimal threshold was important. The optimal value for this threshold was 0.42 of maximum value of difference of brightness between pixels that caused the 83.62% of correlation accuracy. The results showed that graph coloring approach can be a reliable unsupervised approach for brain tumor segmentation. This approach, as an unsupervised approach, shows better accuracy in comparison with neural networks and neuro-fuzzy networks. However, as a limitation, the accuracy of this approach is dependent on the threshold of edges.
对于癌症诊断和治疗规划而言，进行准确可靠的脑肿瘤分割至关重要。脑肿瘤分割的无监督方法较少。本文介绍了一种基于图着色的脑肿瘤分割新无监督方法。在本研究中，图着色方法用于脑肿瘤分割。为此，将脑图像的每个像素视为图的一个节点，并将一对像素的亮度差异视为边。该方法应用于低级别和高级别患者的T1增强磁共振图像。由于图着色需要一个刚性图，必须使用阈值将边划分为存在或不存在的边。该阈值的值影响图像分割的准确性，因此选择最佳阈值很重要。该阈值的最佳值为像素亮度差异最大值的0.42，这导致了83.62%的相关准确率。结果表明，图着色方法可以成为一种可靠的脑肿瘤分割无监督方法。作为一种无监督方法，该方法与神经网络和神经模糊网络相比显示出更好的准确性。然而，作为一个局限性，该方法的准确性取决于边的阈值。
