Entropy Maximization, Stationary Wavelet and DCT Based Segmentation, De-Noising and Progressive Transmission Technique for Diseased MRI Images

Abstract:

We have devised a way of segmentation and progressive transmission of diseased MRI images. We use Particle Swarm Optimization (PSO) to get the region of interest (ROI) of the diseased MRI image. We use the concept of Multi-resolution Wavelet analysis to de-noise the ROI. We use Stationary Wavelet Transform together with Soft Thresholding Technique for de-noising purpose. A variable mask is used to get the segmented image. Varying percentages of DCT coefficients are used for progressive transmission of the diseased MRI image. Clustering of the images using K-Means algorithm result in predominantly two cluster namely that of diseased cells and background. Test on various MRI images show that the small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. The proposed method only transmits the diseased MRI for further diagnosis of the disease and treatment.

• Periodical: Applied Mechanics and Materials (Volume 197)
• Pages: 229-234
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.197.229
• Online since: September 2012
• Authors: Arunava De, Anup Kumar Bhattacharjee, Chandan Kumar Chanda, Bansibadan Maji
• Keywords: Computed Tomography, Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), Entropy, Intensity Contrast, Inverse Stationary Wavelet Transform, Magnetic Resonance Imaging (MRI), Multi-Resolution Wavelet Analysis, Particle Swarm Optimization (PSO), Progressive Transmission, Region of Interest, Soft-Thresholding, Stationary Wavelet Transform (SWT), Within-Class Variance

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