Reza Shams Dilmaghani

A novel blind watermarking of ECG signals on medical images using EZW algorithm.

In this paper, we present a novel blind watermarking method with secret key by embedding ECG signals in medical images. The embedding is done when the original image is compressed using the embedded zero-tree wavelet (EZW) algorithm. The extraction process is performed at the decompression time of the watermarked image. Our algorithm has been tested on several CT and MRI images and the peak signal to noise ratio (PSNR) between the original and watermarked image is greater than 35 dB for watermarking of 512 to 8192 bytes of the mark signal. The proposed method is able to utilize about 15% of the host image to embed the mark signal. This marking percentage has improved previous works while preserving the image details

Novel UWB pulse shaping using prolate spheroidal wave functions

In this paper novel prolate spheroidal wave functions are proposed as pulse shapes for use in impulse radio (ultra-wideband) communications. These classes of functions yields orthogonal pulses and have a constant pulse width regardless of the pulse order. This is an important property since it eliminates inter-symbol interference. An M-ary communications system is considered that employs these pulses, and the generation of these pulses using the eigenfunction form of a self-adjoint operator is proposed. It is also shown that these pulses are suitable for use in pulse position modulation (PPM) ultra wideband (UWB) communication systems.

Wavelet vs. Fourier based UWB Systems

In this paper we analyse the application of wavelet transform as an alternative to the conventional Fourier-based multicarrier UWB systems. In Fourier based multicarrier UWB systems a cyclic prefix (CP) with the same length of the channel impulse response must be added to each symbol in order to convert linear convolution into circular convolution. The CP must consist of identical copies of the transmitted data in each symbol and therefore is a waste of bandwidth and resources. In this paper we present a framework for wavelet based multicarrier UWB systems and it is shown that they do not require the cyclic prefix for transmission and, hence throughput increases. Moreover, a closed form formula is derived to represent convolutions counterpart in the wavelet domain. Finally, a performance comparison of both techniques is provided.

A framework for progressive medical image transmission and compression

Digital radiology places very high demands on the networking and digital storage infrastructure of hospitals. In addition to having quite stringent requirements on the quality of the images displayed to the radiologist, much of the technical challenge resides in the necessity of displaying desired images as rapidly as possible. We present an infrastructure for progressive transmission and compression of medical images, which can refine an initial image by increasing the detail information not only in scale-space, but also in coefficient precision. The approach is based on the embedded zerotree wavelet (EZW) algorithm. This algorithm offers a tremendous amount of flexibility in meeting the bandwidth and image quality constraints in a radiological imaging environment. We propose an application of the EZW algorithm in progressive medical image transmission in which it can specify and control the resolution constraint. The presented method can provide a framework for lossy image compression, with performance far superior to those provided by the standard JPEG algorithm.

Progressive medical image transmission and compression

Digital radiology places very high demands on the networking and digital storage infrastructure of hospitals. In addition to having quite stringent requirements on the quality of the images displayed to the radiologist, much of the technical challenge resides in the necessity of displaying desired images as rapidly as possible. We present an infrastructure for progressive transmission and compression of medical images, which can refine an initial image by increasing the detail information not only in scale-space, but also in coefficient precision. The approach is based on the embedded zerotree wavelet (EZW) algorithm. This algorithm offers a tremendous amount of flexibility in meeting the bandwidth and image quality constraints in a radiological imaging environment. We propose an application of the EZW algorithm in progressive medical image transmission in which it can specify and control both the resolution constraint and rate constraint. The presented method can provide a framework for lossy image compression, with performance far superior to those provided by the standard JPEG algorithm. Also due to the flexibility of the method we will show how any region of interest of an image can be sent progressively.

Multi rate/resolution control in progressive medical image transmission for the region of interest (ROI) using EZW

Current progressive image transmission (PIT) systems can not control both bit rate and resolution constraints in images. Due to the partially localised nature of the wavelet transform, the value of any pixel in the image depends on only a small number of wavelet coefficients. Thus, it is possible to specify an arbitrary region of the image and prevent that region from being badly degraded during the compression process. This can be done by representing coefficients corresponding to those regions in the wavelet space more accurately. This is accomplished by multiplying those values by an arbitrary factor before applying the embedded zerotree wavelets (EZW) coding technique. The EZW algorithm is then effectively applied to, wavelet coefficients to refine and encode the most significant ones in the wavelet space. The information of the image is thus transmitted in several successive stages. It is shown that in a PIT system not only any region of interest (ROI) can be sent and received progressively, but also bitrate and resolution constraints can be controlled simultaneously. This option becomes very effective in interactive communication channels where the available bandwidth is at a premium.

UWB multiple-pulse generator and transmitter

In this paper, a multi-pulse generator which generates four different prolate spheroidal wave functions (PSWF), based on a source signal for use in ultra-wideband (UWB) communication systems is proposed, which is then applied to an M-ary communication system. This class of pulse shape yields orthogonal pulses that have a constant pulse width and bandwidth regardless of the pulse order, which is in contrast to the majority of other orthogonal pulse classes. Due to the presented results, it is now possible to build a cheap and easily reproducible UWB pulse generator.

Choice of wavelet filters for medical image compression and approximation

Wavelet coding has proven to be very effective in signal and image compression, denoising and detection. In wavelet based image coding the choice of wavelets is crucial and determines the coding performance. In this paper we investigate effect of applying different types of wavelet filters belonging to orthogonal and biorthogonal families with different orders on the medical image quality in multiresolution framework. Regularity and linearity of phase response of filters were found to be important factors in choosing the proper filter. Our results show that Antonini filter (7/9) is the best choice and leads to more cancellation of aliasing effect caused in the analysis stage and highest peak signal to noise ratio (PSNR) on medical images.

IEEE EMBS Asian-Pacific Conference on Biomedical Engineering

Current progressive image transmission (PIT) systems can not control both bit rate and resolution constraints in images. Due to the partially localised nature of the wavelet transform, the value of any pixel in the image depends on only a small number of wavelet coefficients. Thus, it is possible to specify an arbitrary region of the image and prevent that region from being badly degraded during the compression process. This can be done by representing coefficients corresponding to those regions in the wavelet space more accurately. This is accomplished by multiplying those values by an arbitrary factor before applying the embedded zerotree wavelets (EZW) coding technique. The EZW algorithm is then effectively applied to, wavelet coefficients to refine and encode the most significant ones in the wavelet space. The information of the image is thus transmitted in several successive stages. It is shown that in a PIT system not only any region of interest (ROI) can be sent and received progressively, but also bitrate and resolution constraints can be controlled simultaneously. This option becomes very effective in interactive communication channels where the available bandwidth is at a premium.

IEEE International Workshop on Biomedical Circuits and Systems

Progressive transmission of biomedical images through a wireless network is extremely useful for fast retrieval of the region of interest (RoI) from large images. To do that, the images have to be compressed as much as possible and then streamed to the receiver. The RoIs are streamed and then sent with a higher priority order. Such a process should not impose any visible error to the data, which may contain important clinical information. The compression has been performed by means of embedded zero tree wavelet (EZW) encoding and a MATLAB-based TCP/IP connection has been established for demonstrating the progressive transmission of the data.