Entropy maximization based segmentation, transmission and Wavelet Fusion of MRI images

Authors:
Arunava De;Anup Kumar Bhattacharjee;Chandan Kumar Chanda;Bansibadan Maji
Affiliations:
Department of Electronics and Communication, National Institute of Technology, Durgapur, India;Department of Electronics and Communication, National Institute of Technology, Durgapur, India;Department of Electrical Engineering, Bengal Engineering and Science University, Shibpur, India;Department of Electronics and Communication, National Institute of Technology, Durgapur, India
Venue:
International Journal of Hybrid Intelligent Systems
Year:
2013

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Abstract

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.