Abstract
The main objective of medical image compression is to attain the best possible fidelity for an available communication and storage [6], in order to preserve the information contained in the image and does not have an error when they are processing it. In this work, we propose a medical image compression algorithm based on Artificial Neural Network (ANN). It is a simple algorithm which preserves all the image data. Experimental results performed at 8 bits/pixels and 12bits/pixels medical images show the performances and the efficiency of the proposed method. To determine the ‘acceptability’ of image compression we have used different criteria such as maximum absolute error (MAE), universal image quality (UIQ), correlation and peak signal to noise ratio (PSNR).
Highlights
DICOM (Digital Imaging and Communications in Medicine) image is composed of two files [15]: header files and image pixel data
[18] A.Younus et al have proposed a hybrid medical image compression technique based on Discrete Cosines Transform (DCT) and Lapped Biorthogonal Transform (LBT)
As shown in a tab, it was found that the peak signal to noise ratio (PSNR) was between 34dB and 50dB
Summary
DICOM (Digital Imaging and Communications in Medicine) image is composed of two files [15]: header files and image pixel data. The main steps of image compression are: pixels transform, quantization and entropy coding [11]. In [9] W.K.Yeo et al have presented their medical image compressed algorithm, which is based on Hebbian process and quantization of the extracted components. In [18] A.Younus et al have proposed a hybrid medical image compression technique based on Discrete Cosines Transform (DCT) and Lapped Biorthogonal Transform (LBT). In [7] S.kuamo et al present an experimental study of some image compression methods and propose a new hybrid method based on a neural network. Put all weights and activation thresholds of the network to random values uniformly distributed in a small range. This initialization is done for one neuron. Set the value of the learning rate to a small positive value
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