Brain tumor is a major cause of an increased transient between children and adults. This article proposes an improved method based on magnetic resonance （MRI） brain imaging and image segmentation. Automated classifi...Brain tumor is a major cause of an increased transient between children and adults. This article proposes an improved method based on magnetic resonance （MRI） brain imaging and image segmentation. Automated classification is encouraged by the need for high accuracy in dealing with a human life. Detection of brain tumor is a challenging problem due to the high diversity in tumor appearance and ambiguous tumor boundaries. MRI images are chosen for the detection of brain tumors as they are used in the determination of soft tissues. First, image preprocessing is used to improve image quality. Second, the multi-scale decomposition of complex dual-wavelet tree transformations is used to analyze the texture of an image. Resource extraction draws resources from an image using gray-level co-occurrence matrix （GLCM）. Therefore, the neuro-fuzzy technique is used to classify brain tumor stages as benign, malignant, or normal based on texture characteristics. Finally, tumor location is detected using Otsu threshold. The performance of the classifier is evaluated on the basis of classification accuracies. The simulated results show that the proposed classifier provides better accuracy than the previous method.展开更多
文摘Brain tumor is a major cause of an increased transient between children and adults. This article proposes an improved method based on magnetic resonance （MRI） brain imaging and image segmentation. Automated classification is encouraged by the need for high accuracy in dealing with a human life. Detection of brain tumor is a challenging problem due to the high diversity in tumor appearance and ambiguous tumor boundaries. MRI images are chosen for the detection of brain tumors as they are used in the determination of soft tissues. First, image preprocessing is used to improve image quality. Second, the multi-scale decomposition of complex dual-wavelet tree transformations is used to analyze the texture of an image. Resource extraction draws resources from an image using gray-level co-occurrence matrix （GLCM）. Therefore, the neuro-fuzzy technique is used to classify brain tumor stages as benign, malignant, or normal based on texture characteristics. Finally, tumor location is detected using Otsu threshold. The performance of the classifier is evaluated on the basis of classification accuracies. The simulated results show that the proposed classifier provides better accuracy than the previous method.
