Automatic Brain Tissue Detection in Mri Images Using Seeded Region Growing

Abstract: This paper presents a neural network-based method for automatic classification of magnetic resonance images (MRI) of brain under three categories of normal, lesion benign, and malignant. The proposed technique consists of six subsequent stages; namely, preprocessing, seeded region growing segmentation, connected component labeling (CCL), feature extraction, feature Dimension Reduction, and classification. In the preprocessing stage, the enhancement and restoration techniques are used to provide a more appropriate image for the subsequent automated stages. In the second stage, the seeded region growing segmentation is used for partitioning the image into meaningful regions. In the third stage, once all groups have been determined, each pixel is labeled according to the component to which it is assigned to. In the fourth stage, we have obtained the feature related to MRI images using the discrete wavelet transform (DWT). In the fifth stage, the dimension of obtained DWT features are reduced, using the principal component analysis (PCA), to obtain more essential features. In the classification stage, a supervised feed-forward back-propagation neural network technique is used to classify the subjects to normal or abnormal (benign, malignant). We have applied this method on 2D axial slices of 10 different patient data sets and show that the proposed technique gives good results for brain tissue detection and is more robust and effective compared with other recent works

3D segmentation of glioma from brain MR images using seeded region growing and fuzzy c-means clustering

This thesis presents two algorithms for brain MR image segmentation. The images used are axial MR images of the human brain. The images show a glioma. The objective is to segment the tumour and edema surrounding it from the images. Initially the images are pre-processed by contrast adjustment. Segmentation is performed by two algorithms: seeded region growing and fuzzy c-means clustering. After the images are segmented, the volumes of the segmented regions are measured. The segmentation is done in MATLAB. Finally the results are rendered in 3D in AMIRA