Segmentation of Brain Tumor From MRI Images
Using Fuzzy C-Means Clustering and Seeded Region Growth
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In this book, a segmentation method for automatic detection and extraction of brain tumor from Magnetic Resonance Imaging (MRI) images using fuzzy c-means (FCM) clustering and seeded region growth is being presented. The MRI technique provides an excellent contrast between the brain tissues and the tumour tissues. The area containing the tumour is brighter in intensity than the area of healthy brain tissues. The image processing techniques and algorithms are applied on the MRI images of the brain tumour. These images are provided as input to the MATLAB software and pre-processing operations vi...
In this book, a segmentation method for automatic detection and extraction of brain tumor from Magnetic Resonance Imaging (MRI) images using fuzzy c-means (FCM) clustering and seeded region growth is being presented. The MRI technique provides an excellent contrast between the brain tissues and the tumour tissues. The area containing the tumour is brighter in intensity than the area of healthy brain tissues. The image processing techniques and algorithms are applied on the MRI images of the brain tumour. These images are provided as input to the MATLAB software and pre-processing operations viz., image resizing and conversion to grayscale are applied on this input image. The image is then enhanced using the two-dimensional wavelet decomposition in such a way that the finer details in the image are improved. After the enhancement operation, fuzzy c-means clustering (FCM) is applied for segmentation, which divides the image into a number of clusters, on the basis of intensity values of the pixels.
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