Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

—Image segmentation is challenging task in field of medical image processing. Magnetic resonance imaging is helpful to doctor for detection of human brain tumor within three sources of images (axil, corneal, sagittal). MR images are nosier and detection of brain tumor location as feature is more complicated. Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; therefore contour has been generated. Then to classify tumor for segmentation hybrid Fuzzy K Mean-Self Organization Mapping (FKM-SOM) for variation of intensities is used. For improved segmented accuracy, classification has been performed, mainly features are extracted using Discrete Wavelet Transformation (DWT) then reduced using Principal Component Analysis (PCA). Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall. 

图像分割是医学图像处理领域极具挑战性的任务。磁共振成像（Magnetic Resonance Imaging）可辅助医生检测人脑肿瘤，所用图像包含三种成像方位：轴位、冠状位与矢状位。磁共振图像噪声较多，且作为特征的脑肿瘤位置检测难度更高。虽已有水平集（Level Set）方法被应用，但该方法需人工交互，易受人为因素影响，仅能在不连续区域生成适配轮廓；而通过二值化处理并移除无关对象后，可高亮显示病变人脑肿瘤区域，进而生成准确轮廓。随后，为实现肿瘤分割的分类任务，研究人员采用了针对强度变化的混合模型——模糊K均值-自组织映射（Fuzzy K Mean-Self Organization Mapping, FKM-SOM）。为提升分割精度，研究人员先通过离散小波变换（Discrete Wavelet Transformation, DWT）提取特征，再利用主成分分析（Principal Component Analysis, PCA）完成特征降维。本数据集的每张图像共提取13项特征，随后采用支持向量机（Support Vector Machine, SVM）的核函数分类方法（含径向基核、线性核与多项式核）开展分类实验；最终通过F值（Fscore）、精确率（Precision）、准确率（Accuracy）、特异度（Specificity）与召回率（Recall）等评价指标验证了实验结果。