K Kavin Kumar, T Meera Devi, S Maheswaran
Department of Electronics and communication Engineering, Kongu Engineering College, Perundurai, Erode -638 060, Tamil Nadu, India. Email:
Asian Pac J Cancer Prev. 2018 Oct 26;19(10):2789-2794. doi: 10.22034/APJCP.2018.19.10.2789.
Objective: Detection and classification of abnormalities in Magnetic Resonance (MR) brain images in medical field is very much needed. The proposed brain tumor classification system composed of denoising, feature extraction and classification. Noise is one of the major problems in the medical image and due to that retrieval of useful information from the image is difficult. The proposed method for denoising an image is PURE-LET transform. Methods: This method preserves the diagnostic property of the images. In feature extraction, combination of Modified Multi-Texton Histogram (MMTH) and Multi-Texton Microstructure Descriptor (MTMD) is used and then Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM)are used to extract the feature from the image to compare performance. In classification, classifiers like Support Vector Machine (SVM), K Nearest Neighbors (KNN) and Extreme Learning Machine (ELM)are trained by the extracted features and are used to classify the images. Result: The performance of feature extraction methods with three different classifiers are compared in terms of the performance metrics like sensitivity, specificity, and accuracy. Conclusion: The result shows that the combination of MMTH and MTMD with SVM shows the highest accuracy of 95%.
医学领域中磁共振（MR）脑图像异常的检测与分类非常必要。所提出的脑肿瘤分类系统由去噪、特征提取和分类组成。噪声是医学图像中的主要问题之一，正因如此，从图像中检索有用信息很困难。所提出的图像去噪方法是PURE-LET变换。方法：该方法保留了图像的诊断特性。在特征提取中，使用了改进的多纹理直方图（MMTH）和多纹理微观结构描述符（MTMD）的组合，然后使用灰度共生矩阵（GLCM）和灰度游程长度矩阵（GLRLM）从图像中提取特征以比较性能。在分类中，支持向量机（SVM）、K近邻（KNN）和极限学习机（ELM）等分类器通过提取的特征进行训练，并用于对图像进行分类。结果：根据灵敏度、特异性和准确性等性能指标，比较了三种不同分类器的特征提取方法的性能。结论：结果表明，MMTH和MTMD与SVM的组合显示出最高准确率，为95%。
