Medical imaging is one of the most daunting, challenging, and emerging research topics in image processing. Segmenting the glioma from the brain magnetic resonance images (MRI) is an important and demanding task, as it assists the medical experts for the disease diagnosis process. Recent research methods in image segmentation have highlighted the prospective of graph-based techniques for medical applications. As graph cut (GC) method is interactive in nature, it requires manual selection of the initial kernels for processing. The popularity of the GC method is limited by the occurrence of small cuts due to its shrinkage behavior leading to inaccurate extraction causing erroneous regions. This paper addresses the open research issue of shrinkage behavior by proposing the gradient based kernel selection (GBKS) GC method emphasizing on the directive inclination of the intensity scales. The proposed technique aids in the initialization of GC, removes the shrinkage problem, and locates the tumor in brain images without any human intervention. The performance results of the proposed GBKS GC method are evaluated on high-grade glioma and low-grade glioma MRI images and are analyzed and compared by using various measures. All the results present a remarkable improvement with GBKS GC technique over other existing techniques.

中文翻译：

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使用基于梯度的核选择图切割技术从 MR 图像中提取胶质瘤

医学成像是图像处理中最艰巨、最具挑战性和新兴的研究课题之一。从脑磁共振图像 (MRI) 中分割胶质瘤是一项重要且艰巨的任务，因为它可以协助医学专家进行疾病诊断过程。最近的图像分割研究方法突出了基于图的技术在医学应用中的前景。由于图切割 (GC) 方法本质上是交互式的，因此需要手动选择初始内核进行处理。GC 方法的普及受到小切口的发生的限制，因为其收缩行为会导致提取不准确，从而导致错误区域。本文通过提出基于梯度的核选择 (GBKS) GC 方法来解决收缩行为的开放研究问题，强调强度尺度的定向倾斜。所提出的技术有助于GC的初始化，消除收缩问题，并在没有任何人为干预的情况下在大脑图像中定位肿瘤。所提出的 GBKS GC 方法的性能结果在高级别胶质瘤和低级别胶质瘤 MRI 图像上进行了评估，并通过使用各种措施进行了分析和比较。所有结果都表明 GBKS GC 技术比其他现有技术有了显着的改进。所提出的 GBKS GC 方法的性能结果在高级别胶质瘤和低级别胶质瘤 MRI 图像上进行了评估，并通过使用各种措施进行了分析和比较。所有结果都表明 GBKS GC 技术比其他现有技术有了显着的改进。所提出的 GBKS GC 方法的性能结果在高级别胶质瘤和低级别胶质瘤 MRI 图像上进行了评估，并通过使用各种措施进行了分析和比较。所有结果都表明 GBKS GC 技术比其他现有技术有了显着的改进。