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Multimodal Magnetic Resonance Imaging (MRI) is an imaging technique widely used in the diagnosis and treatment planning of patients. Lesion segmentation of brain MRI is one of the most important image analysis task in medical imaging. In this paper, a new method for the supervised segmentation of the lesion in brain MRI using Grammatical Bee Colony (GBC) is proposed. The segmentation process is adversely affected by the presence of noises and intensity inhomogeneities in the Magnetic Resonance (MR) images. Therefore, noises are removed from the images and intensity inhomogeneities are corrected in the pre-processing steps. A set of stationary wavelet features are extracted from the co-registered 1-weighted (-), 2-weighted (-) and Fluid-Attenuated Inversion Recovery (FLAIR) images after skull stripping. A classifier is evolved using the GBC to classify the tissues as healthy tissues or lesions. The GBC classifier is trained with extracted features. The trained classifier is used to segment the test Magnetic Resonance (MR) image into healthy tissues or lesion regions. Finally, the connected component labeling algorithm is used to extract the lesions from the segmented images in the post-processing step. Effectiveness of the proposed method is tested by identifying the brain lesions from a set of MR images. [ABSTRACT FROM AUTHOR]

An improved glowworm swarm optimization algorithm with generalized opposition-based learning is proposed in this paper and is used in segmentation for magnetic resonance images. Noises are removed and intensity inhomogeneities are corrected in the MR images. Next, a clustering technique with glowworm swarm optimization algorithm with generalized opposition based learning is used. Finally, lesions are separated from the normal tissues of the brain in the post-processing step. The performance of the proposed methodology based on both numerical and visual results are compared with K-means and particle swarm optimization based methodologies over two sets of MR images. The experimental results demonstrate that the proposed methodology statistically outperforms other methodologies. [ABSTRACT FROM AUTHOR]

This paper presents a new method for the segmentation of Magnetic Resonance Imaging (MRI) of brain tumor. First, discrete wavelet transform (DWT)-based soft-thresholding technique is used for removing noise in the MRI. Second, intensity inhomogeneity (IIH) independent of noise is removed from the MRI image. Third, again DWT is used to sharpen the de-noised and IIH corrected image. In this method, the image is decomposed into first level using wavelet decomposition and approximate values are assigned to zero and reconstruct the image results in detailed image. The detailed image is added with the pre-processed image to produce sharpened image. Entropy maximization using Grammatical Swarm (GS) algorithm is used to obtain a set of threshold values and a threshold value is selected with the expert knowledge to separate the lesion part from the other non-diseased cells in the image. [ABSTRACT FROM AUTHOR]