SUMALATHA R - Academia.edu (original) (raw)

Papers by SUMALATHA R

Region Based Coding (RBC) technique is significant for medical image compression and transmission... more Region Based Coding (RBC) technique is significant for medical image compression and transmission. Lossless compression schemes with secure transmission play a key role in telemedicine applications that help in accurate diagnosis and research. In this paper we propose a lossless compression approach based on 3D integer wavelet transform, 3D SPIHT algorithm of MR images. The use of lifting scheme allows to generate truly lossless integer to integer wavelet transforms. The main objective of this work is rejects the noisy background and reconstructs the image portion losslessly. In this work different integer wavelet transforms will be used to compress the 3D MR images. The performance of the system has been evaluated based on bits-per-pixel and peak signal-to-noise ratio.

— In this paper we propose an efficient region of interest (ROI) coding technique based on multiw... more — In this paper we propose an efficient region of interest (ROI) coding technique based on multiwavelet transform, set partitioning in hierarchial (SPIHT) algorithm of medical images. This new method reduces the importance of background coefficients in the ROI code block without compromising algorithm complexity. By using this coding method the compressed bit stream are all embedded and suited for progressive transmission. Extensive experimental results show that the proposed algorithm gives better quality if images using multiwavelets compared to that of the scalar wavelets. The performance of the system has been evaluated based on bits per pixel (bpp) , peak signal to noise ratio (PSNR)and mean square error (MSE).

This paper presents an Adaptive Multiwavelet Transform (AMWT) for region of interest (ROI) based ... more This paper presents an Adaptive Multiwavelet Transform (AMWT) for region of interest (ROI) based medical image compression using set partitioning in hierarchical trees (SPIHT) algorithm. Due to multiple scaling and multiple wavelet functions the AMWT satisfies the orthogonality and symmetry properties. The AMWT is designed with adaptive lifting scheme. The proposed lifting scheme consists of adaptive prediction filter and fixed update filter. The prediction filter produces its prediction from the two previous samples in the adaptive lifting scheme. The proposed prediction filter reduces the energy of one of the decomposition band and also exploits the local image properties for perfect reconstruction of medical images. Experimental results shows that peak signal to noise ratio (PSNR), Correlation Coefficient (CC), Mean Structural Similarity Index (MSSIM) performance is high and Root Mean Square Error (RMSE), Mean Absolute Error (MAE) values are high, and moderate Compression Ratio (CR) at high bits per pixel (BPP) when compared to the integer wavelet and multiwavelet transform.

The main aim of hierarchical lossless image compression is to improve accuracy, reduce the bit ra... more The main aim of hierarchical lossless image compression is to improve accuracy, reduce the bit rate and improve the compression efficiency for the storage and transmission of the medical images while maintain an acceptable image quality for diagnosis purpose. The cost and limitation in bandwidth of wireless channels has made compression is necessity in today's era. In medical images, the contextual region is an area which contains an important information and must be transmitted without distortion. In this paper the selected region of the image is encoded with Adaptive Multiwavelet Transform AMWT) using Multi Dimensional Layered Zero Coding (MLZC). Experimental results shows that Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), Mean Structural Similarity Index (MSSIM) performance is high and Root Mean Square Error (RMSE), Mean Absolute Error (MAE) values are low, and moderate Compression Ratio (CR) at high Bits Per Pixel (BPP) when compared to the integer wavelet and multiwavelet transform.

— In medical image processing the quality of the image is degraded in the presence of noise espec... more — In medical image processing the quality of the image is degraded in the presence of noise especially in ultra sound imaging and Magnetic resonance imaging the data was corrupted by signal dependent noise known as salt and pepper noise. Removal of noise from the medical images is a critical issue for analysis and interpretation. In this work a new technique called Spatial Adaptive Mask Filter is proposed. The proposed filter improves the quality of MRI and ultra sound images. Experimental results show that the proposed filter outperforms than the mean, median, and adaptive median filters in terms of mean square error (MSE) and peak signal to noise ratio (PSNR). Keywords— medical image processing; mean; median; adaptive median; spatial adaptive mask filter component;