The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

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Soft starters are used with induction motors in blowers, fans, pumps and the crane hoist drives. AC voltage controllers are used as soft starters in induction motors for starting and to adjust its speed. This paper presents a novel neuro fuzzy based ac voltage controller to generate the firing pulses for appropriate thyristors for any given operating torque, speed of the motor and the load. An ANFIS (Adaptive Neuro Fuzzy Inference System) model has been designed to achieve the proposed algorithm. MATLAB/SIMULINK package has been used to simulate the proposed method. Simulation results presented in this paper explain the advantages of proposed soft starting method over conventional method. The advantages of proposed method are its simplicity, stability, and accuracy and fast response.