Brain Tumor Segmentation Using Graph Coloring Approach in Magnetic Resonance Images
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 122
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شناسه ملی سند علمی:
JR_JMSI-11-4_008
تاریخ نمایه سازی: 28 تیر 1402
چکیده مقاله:
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 T۱‑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 ۰.۴۲ of maximum value of difference of
brightness between pixels that caused the ۸۳.۶۲% 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.
کلیدواژه ها:
نویسندگان
Rouhollah Bagheri
Department of Management, Ferdowsi University of Mashhad, Iran
Jalal Haghighat Monfared
Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Mohammad Reza Montazeriyoun
Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran