Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohsen Mohammadvali’ee; Ali Mahloojifar
Volume 7, Issue 3 , June 2013, , Pages 265-276
Abstract
One of the most important goals for increasing the recognition and treatment revenue is transmitting the vital data to medical care team, more quickly. Nowadays, use of new technologies for transmission of data is extending every day. In this research, for transmitting electrocardiogram, first we code ...
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One of the most important goals for increasing the recognition and treatment revenue is transmitting the vital data to medical care team, more quickly. Nowadays, use of new technologies for transmission of data is extending every day. In this research, for transmitting electrocardiogram, first we code the signal into a matrix of codes, then we will use bluetooth technology to transmit data from offset device to target device. Signal coding will affect in sending and storing data. This suite of codes that form for the first time in this method, include number and type of extermumes, time of occurring them, samples of signal and etc. We complete the coding, using arithmetic coding. The input of arithmetic coding is the extracted suite of coefficients and the output is arithmetic codes. We use SD-200 serial bluetooth modules produced by SENA™ in transmission of coding coefficients. The transmitter sends extracted coefficients and receptor receives them and reconstructs the primary signal. For testing and evaluating the method, we use MIT–BIH arrhythmia database. In our method, when average Percentage of Root Mean Square Differential (PRD) is equal to 5.93%, Compression Ratio (CR) and Cross Correlation (CC) is equal to 8.69 and 99.8%, respectively. Beside, when PRD is about 10.21%, CR and CC is 13.03 and 99.47%, respectively. The maximum standard deviation of compression ratio in two states is 4.17.
Biomedical Image Processing / Medical Image Processing
Emadoddin Fatemizadeh; Parisa Shooshtari
Volume 2, Issue 3 , June 2008, , Pages 191-201
Abstract
Nowadays due to the huge capacity and bandwidth essentials for medical images, communications and storage purposes, medical images compression is one of most important concepts in this area. Error free compression techniques have the weakness of low compression ratio. On the other hand, lossy techniques ...
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Nowadays due to the huge capacity and bandwidth essentials for medical images, communications and storage purposes, medical images compression is one of most important concepts in this area. Error free compression techniques have the weakness of low compression ratio. On the other hand, lossy techniques with high compression ratio result in low quality of the images. In recent years, some special compression schemes have been suggested by splitting the original image into two regions: Region of Interest (ROI) with lossless compression and the Region of Background (ROB) with lossy compression and a lower quality. In this paper, we proposed a novel selective compression approach to compress 3D brain MR images. For this purpose, an adaptive mesh for the first slice was designed and estimation of the gray levels of the next slices was performed through deformations of the mesh elements. After residual image determination, the error between the original image and the approximated image was transformed to the wavelet domain using a region-based discrete wavelet transform (RBDWT). Finally, the wavelet coefficients were coded by an object-based SPIHT coder.
