Human brain tumors detection using neutrosophic c-means clustering algorithm

Nihal N. Mostafa
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Abstract

For the last several decades, detecting human brain tumors has evolved into one of the most difficult problems in the field of medical research. In the realm of medical image processing, the categorization of brain tumors is a difficult job to do. In this research, we offer a model for the detection of human brain tumors in magnetic resonance imaging (MRI) images that makes use of the template-depend neutrosophic c-means and is compared with the fuzzy C means method. This model is referred to as the NCM method. In this suggested method, well first of all, the pattern K-means method is used to initialize segmentation markedly through the ideal choice of a template, depending on the gray-level intensity of the image; besides which, the revised membership is calculated by the ranges from the closest centroid to cluster pieces of data by using neutrosophic C-means (NCM) method while it approaches its perfect outcomes; and at last, the NCM clustering method is used for sensing tumor positron emission tomography (PET) imaging The findings of the simulation reveal that the suggested method can produce improved identification of pathological and normal cells in the human brain despite a little separation in the intensity of the grey level.
利用嗜中性c均值聚类算法检测人脑肿瘤
在过去的几十年里,检测人类脑肿瘤已经发展成为医学研究领域最困难的问题之一。在医学图像处理领域,脑肿瘤的分类是一项困难的工作。在本研究中,我们提出了一个利用依赖模板的中性粒细胞C均值在磁共振成像(MRI)图像中检测人脑肿瘤的模型,并与模糊C均值方法进行了比较。这个模型被称为NCM方法。在这个建议的方法中,首先,模式k -均值方法通过模板的理想选择来初始化分割,这取决于图像的灰度强度;此外,修正后的隶属度是在接近完美结果时,用中性c均值(NCM)方法计算离聚类数据最近的质心到聚类数据块的距离;最后,将NCM聚类方法应用于肿瘤正电子发射断层扫描(PET)成像的检测。仿真结果表明,该方法可以提高人脑病理细胞和正常细胞的识别能力,尽管灰度强度存在一定的分离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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