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.

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We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent sections using similarity of neighbourhood pixels. Tests on various diseased MRI images show that small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. Previous works are based on bimodal images whereas our work is based on multimodal images.

A method of progressive transmission of Magnetic Resonance Image with lesions over long distances is proposed. The Magnetic Resonance Images at the transmitter end are segregated on the basis of presence of lesions. Entropy Maximization using Hybrid Particle Swarm Optimization algorithm that incorporates a Wavelet theory based mutation operation is used for segmentation of Magnetic Resonance Images. It applies the Multi-resolution Wavelet theory to overcome the stagnation phenomena of the Particle Swarm Optimization. Thus the segmentation algorithm using Hybrid Particle Swarm Algorithm explores the solution space more effectively for a better solution. Varying percentages of Discrete Cosine Transform coefficients of segmented Magnetic Resonance Images are used for progressive image transmission. For a particular image data, the progressively received images are of different resolutions. At the receiver's end the progressively received images of different resolutions are fused using Multi-resolution wavelet analysis to get a visually suitable image for diagnosis. The doctor or a radiologist identifies a particular class of image with lesions and may ask for the entire un-segmented Magnetic Resonance Image dataset of a particular patient for further diagnosis. The proposed system helps to reduce the load on the system by choosing not to transmit the Magnetic Resonance Images without lesions.

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