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Tumor Classification using Automatic Multi-Thresholding

机译:使用自动多阈值进行肿瘤分类

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In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly.The Otsu classification algorithm solves the given medical image by continuously separating the input gray-level image by multi-thresholding until reaching optimal smooth rate. The method solves exactly the problem of the uncertain contoured objects in medical image by using the Otsu clustering classification with automatic multi-thresholding operation.
机译:在本文中,我们探索了用于医学图像应用的这些数学方法。所提出的方法在检测肿瘤中的应用将能够准确地区分肿瘤的大小和区域。在这项研究中,开发了一些用于医学脑图像的肿瘤对象检测方法的主要设计和实验结果,以利用直方图分析和Otsu聚类相结合的自动多阈值方法来解决此问题。直方图评估可以首先确定聚类的最佳数量。Otsu分类算法通过多阈值连续分离输入的灰度图像,直到达到最佳平滑率,从而解决了给定的医学图像。该方法通过采用自动多阈值操作的Otsu聚类分类,准确地解决了医学图像中轮廓物体不确定的问题。

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