肿瘤是一种异常组织的增长可以区分从周围组织的结构。肿瘤也可能导致癌症,这是一个主要的主要死亡原因,全球负责所有死亡的13%左右。癌症发病率在世界上以惊人的速度增长。伟大的知识和经验在放射学在医学成像所需的准确的肿瘤检测。肿瘤检测的自动化是必需的,因为可能有熟练的放射科医生短缺的需要。综述了流程和技术用于检雷竞技苹果下载测肿瘤根据医学成像结果如乳房x光检查,x射线计算机断层扫描(x射线CT)和磁共振成像(MRI)。MRI图像的磁共振成像是也可以用于diagnosisand发现人类大脑的类型,测试是正常或异常。此外,MRI图像给出一个观察和有用的信息,这将有助于医生和手术,以避免一些错误检测和诊断的过程中会发生。同时,MRI特征用于避免人为错误在手册的解释医学内容。最受欢迎的和有用的应用程序,该应用程序需要的医疗系统是核磁共振脑图像分类方法。 In this paper, we proposed a new clustering algorithm which relies on the differences between the contrast level of the tumor in the MRI. We depend on new approach for image clustering which is based on the difference between the construct (which is the intensity level) between the tumor region and the whole MRI image. In contrast, other algorithms like k-means, Fuzzy c-means, or probabilistic c-means depend on typical approach which is based on the distance majority between each point (pixel) and its mean. Our work consists some preprocessing steps like smoothed and enhanced the MRI by using enhancement techniques such as Gaussian kernel, median filter, high-pass filter, and Morphological image operation. Mainly, the proposed clustering algorithm in this work uses for segmentation of the image to detect the suspicious region of the tumor in brain MRI image. In this paper feature our results have been compared with the traditional clustering algorithm such as k-means and watershed.
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