Wavelet-based medical image compression with adaptive prediction

Chen, Yao Tien; Tseng, Din-Chang

doi:10.1016/j.compmedimag.2006.08.003

Cited by 49 publications

(13 citation statements)

References 17 publications

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“…By now, several methods have been proposed to deal with medical image compression issues. 7,13,16,25,39,43,50,57 Many of these methods, such as differential pulse code modulation (DPCM), 7,13,57 hierarchical interpolation (HINT), 25,39,43 multiplicative autoregressive models (MAR) 14,16 and stationary full range autoregressive (SFAR), 28 pyramid data structures 34,38 and prediction coding, 12,27 are lossless techniques in which the reconstructed image is exactly the same as the original one.…”

Section: Introductionmentioning

confidence: 99%

Roi-Based 3d Human Brain Magnetic Resonance Images Compression Using Adaptive Mesh Design and Region-Based Discrete Wavelet Transform

Fatemizadeh

Shooshtari

2010

Int. J. Wavelets Multiresolut Inf. Process.

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Due to the large volume required for medical images for transmission and archiving purposes, the compression of medical images is known as one of the main concepts of medical image processing. Lossless compression methods have the drawback of a low compression ratio. In contrast, lossy methods have a higher compression ratio and suffer from lower quality of the reconstructed images in the receiver. Recently, some selective compression methods have been proposed in which the main image is divided into two separate regions: Region of Interest (ROI), which should be compressed in a lossless manner, and Region of Background (ROB), which is compressed in a lossy manner with a lower quality. In this research, we introduce a new selective compression method to compress 3D brain MR images. To this aim, we design an adaptive mesh on the first slice and estimate the gray levels of the next slices by computing the mesh element's deformations. After computing the residual image, which is the difference between the main image and the estimated one, we transform it to the wavelet domain using a regionbased discrete wavelet transform (RBDWT). Finally, the wavelet coefficients are coded by an object-based SPIHT coder.

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Section: Introductionmentioning

confidence: 99%

Roi-Based 3d Human Brain Magnetic Resonance Images Compression Using Adaptive Mesh Design and Region-Based Discrete Wavelet Transform

Fatemizadeh

Shooshtari

2010

Int. J. Wavelets Multiresolut Inf. Process.

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“…In [12] A lossless wavelet-based image compression method with adaptive prediction was proposed, and applied to achieve higher compression rates on CT and MRI images. In [13] a combining technique for image compression based on the Hierarchical Finite State Vector Quantization (HFSVQ) and a neural network, was proposed and applied to medical images.…”

Section: Introductionmentioning

confidence: 99%

Medical radiographs compression using neural networks and Haar wavelet

Khashman

Dimililer

2009

Ieee Eurocon 2009

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Efficient storage and transmission of medicalimages in telemedicine is of utmost importance however, this efficiency can be hindered due to storage capacity and constraints on bandwidth. Thus, a medical image may require compression before transmission or storage. Ideal image compression systems must yield high quality compressed images with high compression ratio; this can be achieved using wavelet transform based compression, however, the choice of an optimum compression ratio is difficult as it varies depending on the content of the image. In this paper, a neural network is trained to relate radiograph image contents to their optimum image compression ratio. Once trained, the neural network chooses the ideal Haar wavelet compression ratio of the x-ray images upon their presentation to the network. Experimental results suggest that our proposed system, can be efficiently used to compress radiographs while maintaining high image quality.

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“…Each pixel value can be predicted or estimated from nearby or neighbouring pixels, then finding the difference between the original and the predicted image, which is referred as the residual, which is normally coded because of the reduced image information compared to the original image [8][9][10][11][12]. Some researcher's efforts aimed to improving the traditional autoregressive model efficiency, including Ghadah [13] in 2012.…”

Section: Introductionmentioning

confidence: 99%

Correlated Hierarchical Autoregressive Models Image Compression

Al-Khafaji¹,

Hasan²,

Noaman³

2017

djps

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In this paper, a Correlated Hierarchical Autoregressive Model (CHARM) method for image compression is proposed. It based on using multi-layered modeling concept of correlated autoregressive coefficients, which is a modified version of Hierarchical Autoregressive Model (HARM). The test results indicate that the suggested techniques improve the compression ratio along with preserving the image quality compared to traditional predictive coding or autoregressive model and HARM on a series of selected images.

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Wavelet-based medical image compression with adaptive prediction

Cited by 49 publications

References 17 publications

Roi-Based 3d Human Brain Magnetic Resonance Images Compression Using Adaptive Mesh Design and Region-Based Discrete Wavelet Transform

Roi-Based 3d Human Brain Magnetic Resonance Images Compression Using Adaptive Mesh Design and Region-Based Discrete Wavelet Transform

Medical radiographs compression using neural networks and Haar wavelet

Correlated Hierarchical Autoregressive Models Image Compression

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