Brain cancer treatment mainly depends on the accurate detection of the tumor type, location, size and borders. Magnetic resonance images (MRI) can be used to analyze the properties of the desired region such as tissues and tumors with automated and semi-automated approaches. So, the extraction of MRI brain tumor image is a challenging task in medical image processing. The major problems associated with MRI analysis by a physician are time consuming and the accuracy depends on the expertise of the physician.This limitation can be overcome by the computer aided diagnosis (CAD) technology. In this paper, a CAD system is designed to detect brain tumors with computer assistance using T1 and T2 weighted MR images. The designed system classifies the tumor into benign or malignant from MR Image using a novel automated method which increases the performance and reduces the complexity involved in the tumor diagnosis. The CAD system has four stages such as image acquisition, segmentation, feature extraction and classification. Segmentation is done with the help of K-means clustering, which enhances the medical image and the clustering quality to avoid local optima and to find global optima. The feature extraction is performed by gray level co-occurrence matrix (GLCM). The tumor classification is done using support vector machine (SVM) and bag of visual words (BOVW) classifiers. The test result of the SVM classifier gives accuracy 95.0%, sensitivity 91.79% and specificity 94.75%. Whereas, the BOVW classifier yields results of accuracy 96.0%, sensitivity 90.0% and specificity 100%. This result shows that, the designed system classifies the tumor into benign or malignant with a good level of accuracy.

脑癌的治疗主要取决于对肿瘤类型，位置，大小和边界的准确检测。磁共振图像（MRI）可用于通过自动化和半自动化方法分析所需区域（例如组织和肿瘤）的属性。因此，MRI脑肿瘤图像的提取在医学图像处理中是一项艰巨的任务。医生进行MRI分析的主要问题是耗时，准确性取决于医生的专业知识。可以通过计算机辅助诊断（CAD）技术克服这一局限性。在本文中，设计了一个CAD系统，以计算机辅助使用T1和T2加权MR图像检测脑部肿瘤。设计的系统使用新颖的自动化方法将肿瘤从MR图像分类为良性或恶性，从而提高性能并降低涉及肿瘤诊断的复杂性。CAD系统具有四个阶段，例如图像采集，分割，特征提取和分类。借助K-means聚类进行分割，可增强医学图像和聚类质量，从而避免局部最优并找到全局最优。特征提取是通过灰度共生矩阵（GLCM）执行的。使用支持向量机（SVM）和视觉词袋（BOVW）分类器对肿瘤进行分类。SVM分类器的测试结果显示准确度为95.0％，灵敏度为91.79％，特异性为94.75％。而BOVW分类器得出的结果的准确度为96.0％，灵敏度为90。0％，特异性100％。该结果表明，所设计的系统以良好的准确度将肿瘤分类为良性或恶性。