biomedical MRI; cancer; fuzzy set theory; image segmentation; medical image processing; pattern clustering; tumours; Delhi; India; MRI images; RGCI-and-RC; Rajiv Gandhi Cancer Institute-and-Research Centre; abnormal tissue area; abnormal tissue location; abnormal tissue orientation; automatic brain tumor detection; brain tumor diagnosis; brain tumor segmentation; error percentage; fuzzy c-means algorithm; ground truth; human body image creation; image acquisition; image segmentation techniques; internal anatomy; k-means algorithm; magnetic resonance imaging; medical imaging technique; medical research; performance analysis; real time database; region growing algorithm; visualization tool; Algorithm design and analysis; Clustering algorithms; Computers; Conferences; Image segmentation; Magnetic resonance imaging; Tumors; Medical imaging; brain tumor segmentation; clustering; region growing;
机译:基于区域生长的磁共振(MR)图像中的形态边缘检测和脑肿瘤分割和改进模糊C型(FCM)算法的性能评价
机译:MRI图像脑肿瘤分割的K值,阈值和区域增长算法的比较分析
机译:基于模糊C-均值聚类和种子区域生长的脑MRI图像肿瘤区域分割
机译:脑肿瘤分割:使用k型,模糊C型算法的性能分析
机译:用于3D脑MRI分割的纹理加权模糊C均值算法的开发
机译:分割视网膜血管的聚类算法K均值和模糊C均值的比较
机译:K平均值,高斯混合模型,模糊C型脑肿瘤细分算法的比较研究