Hybrid DWT-DCT compression algorithm & a new flipping block with an adaptive RLE method for high  medical image compression ratio

Huge number of medical images are generated and needs for more storage capacity and bandwidth for transferring over the networks. Hybrid DWT-DCT compression algorithm is applied to compress the medical images by exploiting the features of both techniques. Discrete Wavelet Transform (DWT) coding is applied to image YCbCr color model which decompose image bands into four subbands (LL, HL, LH and HH). The LL subband is transformed into low and high frequency components using Discrete Cosine Transform (DCT) to be quantize by scalar quantization that was applied on all image bands, the quantization parameters where reduced by half for the luminance band while it is the same for the chrominance bands to preserve the image quality, the zigzag scan is applied on the quantized coefficients and the output are encoded using DPCM, shift optimizer and shift coding for DC while adaptive RLE, shift optimizer then shift coding applied for AC, the other subbands; LH, HL and HH are compressed using the scalar quantization, Quadtree and shift optimizer then shift coding. In this paper, a new flipping block with an adaptive RLE is proposed and applied for image enhancement. After applying DCT system and scalar quantization, huge number of zeros produced with less number of other values, so an adaptive RLE is used to encode this RUN of zeros which results with more compression.Standard medical images are selected to be used as testing image materials such as CT-Scan, X-Ray, MRI these images are specially used for researches as a testing samples. The results showed high compression ratio with high quality reconstructed images

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Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Entropy ◽

10.3390/e22121385 ◽

2020 ◽

Vol 22 (12) ◽

pp. 1385

Author(s):

Roman Starosolski

Keyword(s):

Compression Ratio ◽

Medical Images ◽

Three Dimensional ◽

Discrete Wavelet ◽

Adaptive Wavelet ◽

Entropy Estimation ◽

New Methods ◽

Adaptive Parameter ◽

3D Dwt ◽

Volumetric Medical Images

The primary purpose of the reported research was to improve the discrete wavelet transform (DWT)-based JP3D compression of volumetric medical images by applying new methods that were only previously used in the compression of two-dimensional (2D) images. Namely, we applied reversible denoising and lifting steps with step skipping to three-dimensional (3D)-DWT and constructed a hybrid transform that combined 3D-DWT with prediction. We evaluated these methods using a test-set containing images of modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). They proved effective for 3D data resulting in over two times greater compression ratio improvements than competitive methods. While employing fast entropy estimation of JP3D compression ratio to reduce the cost of image-adaptive parameter selection for the new methods, we found that some MRI images had sparse histograms of intensity levels. We applied the classical histogram packing (HP) and found that, on average, it resulted in greater ratio improvements than the new sophisticated methods and that it could be combined with these new methods to further improve ratios. Finally, we proposed a few practical compression schemes that exploited HP, entropy estimation, and the new methods; on average, they improved the compression ratio by up to about 6.5% at an acceptable cost.

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Backpropagation Neural Network Implementation for Medical Image Compression

Journal of Applied Mathematics ◽

10.1155/2013/453098 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 19

Author(s):

Kamil Dimililer

Keyword(s):

Neural Network ◽

Image Compression ◽

Compression Ratio ◽

Haar Wavelet ◽

Compression Method ◽

High Compression Ratio ◽

Haar Wavelet Transform ◽

X Ray ◽

Ideal Image ◽

And Storage

Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.

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Hybrid multiple watermarking technique for securing medical images of modalities MRI, CT scan, and X-ray

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i3.pp2349-2356 ◽

2020 ◽

Vol 10 (3) ◽

pp. 2349

Author(s):

Imane Assini ◽

Abdelmajid Badri ◽

Aicha Sahel ◽

Abdennaceur Baghdad

Keyword(s):

Medical Image ◽

Medical Images ◽

Singular Values ◽

Hadamard Transform ◽

Discrete Wavelet ◽

Resonance Imaging ◽

X Ray ◽

Multiple Watermarking ◽

Magnetic Resonance Imaging Mri ◽

Value Decomposition

In order to contribute to the security of sharing and transferring medical images, we had presented a multiple watermarking technique for multiple protections; it was based on the combination of three transformations: the discrete wavelet transform (DWT), the fast Walsh-Hadamard transform (FWHT) and, the singular value decomposition (SVD). In this paper, three watermark images of sizes 512x 512 were inserted into a single medical image of various modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and X-Radiation (X-ray). After applying DWT up to the third level on the original image, the high-resolution sub-bands were being selected subsequently to apply FWHT and then SVD. The singular values of the three watermark images were inserted into the singular values of the cover medical image. The experimental results showed the effectiveness of the proposed method in terms of quality and robustness compared to other reported techniques cited in the literature.

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ROI-BASED 3D HUMAN BRAIN MAGNETIC RESONANCE IMAGES COMPRESSION USING ADAPTIVE MESH DESIGN AND REGION-BASED DISCRETE WAVELET TRANSFORM

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691310003559 ◽

2010 ◽

Vol 08 (03) ◽

pp. 407-430 ◽

Cited By ~ 3

Author(s):

EMAD FATEMIZADEH ◽

PARISA SHOOSHTARI

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Compression Ratio ◽

Medical Images ◽

Region Of Interest ◽

Adaptive Mesh ◽

Magnetic Resonance Images ◽

Discrete Wavelet ◽

Object Based ◽

The Difference

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 region-based discrete wavelet transform (RBDWT). Finally, the wavelet coefficients are coded by an object-based SPIHT coder.

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3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽

10.3390/s21051817 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1817

Author(s):

Jiawen Xue ◽

Li Yin ◽

Zehua Lan ◽

Mingzhu Long ◽

Guolin Li ◽

...

Keyword(s):

Image Compression ◽

Compression Ratio ◽

Signal To Noise Ratio ◽

Blocking Artifacts ◽

Compression Method ◽

High Compression Ratio ◽

Multiplication Operation ◽

3D Data ◽

Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

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