Brain Tumor Segmentation In MRI Image Using Unsupervised Artificial Bee Colony And FCM Clustering

Tumor segmentation of MRI Brain images is still a challenging problem. This paper proposes a fast MRI Brain image segmentation method based on Artificial Bee Colony (ABC) algorithm. The value in a continuous gray scale interval is searched using threshold estimation. The optimal threshold value is searched with the help of ABC algorithm . In order to get an efficient fitness function for ABC algorithm, after the definition of grey number in Grey theory, the original image is decomposed by discrete wavelet transform. Then, a filtered image is produced by performing a noise reduction to the approximation image reconstructed with low-frequency coefficients. At the same time, a gradient image is reconstructed with some high-frequency coefficients. A co-occurrence matrix based on the filtered image and the gradient image is therefore constructed, and an improved two-dimensional grey entropy is defined to serve as the fitness function of ABC algorithm.Then Fuzzy-C Means algorithm is used for clustering the segmented image.