Compression Method For Medical Images Research Articles

Comparison of the Packet Wavelet Transform Method for Medical Image Compression

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.

Comparison of the Packet Wavelet Transform Method for Medical Image Compression

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.

Comparison of the Packet Wavelet Transform Method for Medical Image Compression

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.

YOLO-based ROI selection for joint encryption and compression of medical images with reconstruction through super-resolution network

Medical images contain significant patient information, and this confidential data should not be accessed without proper authorisation. Concurrently, due to the high redundancy of image data, compression is necessary to minimise image size and efficiently utilise network resources. This paper presents an effective joint encryption and compression method for medical images that prevent critical data leakage while reducing redundancy. Initially, a powerful real-time object detection method, You Only Look Once v7, is employed to accurately and swiftly detect the region of interest (ROI) within the medical images. Subsequently, a joint three-dimensional chaotic map and Huffman encoding are applied to secure medical images without compromising the compression ratio or increasing the time cost. Lastly, a super-resolution network is established at the receiver end to better reconstruct the ROI image for precise diagnostic purposes. The comprehensive experimental analysis demonstrates that our method delivers high levels of security, compression, and visual quality performance on standard datasets used in smart healthcare applications, at a minimum. Furthermore, our approach outperforms other competitive state-of-the-art schemes when compared. We hope this study will inspire further research within the healthcare community.

Robust Medical Image Compression Method Based on Improved Integer Wavelet Transform for Speedy Transmission

Robust Medical Image Compression Method Based on Improved Integer Wavelet Transform for Speedy Transmission

An Improved Medical Image Compression Method Based on Wavelet Difference Reduction

Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time.

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Rate-distortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).

On a hybrid lossless compression technique for three-dimensional medical images.

In the last two decades, incredible progress in various medical imaging modalities and sensing techniques have been made, leading to the proliferation of three‐dimensional (3D) imagery. Byproduct of such great progress is the production of huge volume of medical images and this big data place a burden on automatic image processing methods for diagnostic assistance processes. Moreover, large amount of medical imaging data needs to be transmitted with no loss of information for the purpose of telemedicine, remote diagnosis etc. In this work, we consider a hybrid lossless compression technique with object‐based features for three‐dimensional (3D) medical images. Our approach utilizes two phases as follows: first we determine the volume of interest (VOI) for a given 3D medical imagery using selective bounding volume (SBV) method, and second the obtained VOI is encoded using a hybrid lossless algorithm using Lembel‐Ziv‐Welch Coding (LZW) followed by arithmetic coding (L to A). Experimental results show that our proposed 3D medical image compression method is comparable with other existing standard lossless encoding methods such as Huffman Coding, Run Length Coding, LZW, and Arithmetic Coding and obtains superior results overall.

Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion.

Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio ( ), structural similarity ( ), and compression ratio ( ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. The results show that the method can significantly improve of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of and are 70dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. We thus conclude that the method has a high potential to be applied in teleintervention applications.

Performance analysis of a new fractal compression method for medical images based on fixed partition

Applications of image compression have been gaining importance over the years in medical fields. The medical imaging modalities produced a large amount of information on all levels of hospital care. This information, in the form of images, needs to be stored for future references. While compressing the medical images, it is necessary to maintain high diagnostic quality with a high compression rate. Several image compression approaches have been developed towards the direction of the storage space problem in such a way that the doctors accurately and reliably diagnose the patient’s diseases from the reconstructed image after decompression. A high compression ratio is required to reduce the storage space due to the large size of medical images. Fractal image compression is a lossy technique to compress the image in a coded form instead of pixels and is differentiated by its long encoding time with a high compression ratio, resolution-independent, fast decoding, and self-similarity. The main purpose of this paper is to present a comparative performance study of the three coding schemes of fractal compression for grayscale medical images based on fixed partition. The first two coding schemes are based on the pixel-pattern measure and the third scheme is a proposed method based on the fractal dimension for complexity measure of range and domain blocks. The comparative study included encoding time, peak signal to noise ratio, and compression ratio, as a result, has been accomplished.

A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data.

RGB-Based Compressed Medical Imaging Using Sparsity Averaging Reweighted Analysis for Wireless Capsule Endoscopy Images

Compressed medical imaging (CMI) is a medical image sampling process with several samples lower than the Nyquist-Shannon sampling theorem for efficient image sampling; therefore, speeds up the processing time of medical applications. In comparison to previous approaches focusing on single-layer images analysis, this paper proposes CMI using RGB-based sparsity averaging with reweighted analysis (RGB-SARA). The proposed RGB-SARA method is based on the spread spectrum (SS) sampling method, sparsity averaging (SA), basis pursuit denoise (BPDN) reconstruction method, and reweighted analysis (RA). The CS-based SS sampling method compresses each sample in the specific RGB layer followed by SA and BPDN with RA as a sparsity basis and to enhance the performance of CMI reconstruction, respectively. A detailed results analysis is presented in terms of signal-to-noise ratio (SNR), average SNR (ASNR), structural similarity index (SSIM), and processing time demonstrating the efficacy of the proposed RGB-SARA over conventional CMI, i.e., Haar, Daubechies 8 (Db8), and curvelet. A successful demonstration is presented proving that the proposed RGB-SARA is a potential of a new compression method for medical images with high visual quality.

Survey on different methods in image compression of Brain Images

The survey of brain and medical image compression methods. Reduce the size of image as image compression. Necessity and importance of compression of an image has been discussed. Application of the lossy compression technique is multimedia data. Various compression approaches are discussed for two categories. Also brain image compression techniques are highlighted, in addition with, quantitative comparisons between different compression methods. Also advantages and disadvantages of each method are discussed.

Lossless Hybrid Coding technique based on Quasi Fractal & Oscillation Concept Method for Medical Image Compression

The Image compression is the most important entity in various fields. Image compression plays vital role in many applications. Out of which biomedical is one of the challenging applications. In medical research, everyday there is fast development and advancement. Medical researchers are thinking about digital storage of data hence medical image compression has a crucial role in hospitals. Here Morphological filter & adaptive threshold are used for refinement and used Quasi Fractal & Oscillation concept for developing new hybrid algorithm. Oscillation concept is lossy image compression technique hence applied on Non-ROI. Quasi fractal is lossless image compression technique applied on ROI. The experimental results shows that better CR with acceptable PSNR has been achieved using hybrid technique based on Morphological band pass filter and Adaptive thresholding for ROI. Here, innovative hybrid technique gives the CR 24.61 which improves a lot than hybrid method using BTC-SPIHT is 5.65. Especially PSNR is also retained and bit improved i.e. 33.51. This hybrid technique gives better quality of an image.

Hybrid medical image compression method using quincunx wavelet and geometric actif contour

The purpose of this article is to find an efficient and optimal method of compression by reducing the file size while retaining the information for a good quality processing and to produce credible pathological reports, based on the extraction of the information characteristics contained in medical images. In this article, we proposed a novel medical image compression that combines geometric active contour model and quincunx wavelet transform. In this method it is necessary to localize the region of interest, where we tried to localize all the part that contain the pathological, using the level set for an optimal reduction, then we use the quincunx wavelet coupled with the set partitioning in hierarchical trees (SPIHT) algorithm. After testing several algorithms we noticed that the proposed method gives satisfactory results. The comparison of the experimental results is based on parameters of evaluation.

Improving 3D Medical Image Compression Efficiency Using Spatiotemporal Coherence

Advanced methodologies for transmitting compressed images, within acceptable ranges of transmission rate and loss of information, make it possible to transmit a medical image through a communication channel. Most prior works on 3D medical image compression consider volumetric images as a whole but fail to account for the spatial and temporal coherence of adjacent slices. In this paper, we set out to develop a 3D medical image compression method that extends the 3D wavelet difference reduction algorithm by computing the similarity of the pixels in adjacent slices and progressively compress only the similar slices. The proposed method achieves high-efficiency performance on publicly available datasets of MRI scans by achieving compression down to one bit per voxel with PSNR and SSIM up to 52:3 dB and 0:7578, respectively.

A Hybrid Method for Medical Image Compression in Mobile Picture Archiving and Communication System

A Hybrid Method for Medical Image Compression in Mobile Picture Archiving and Communication System

TONE COMPRESSION ALGORITHM FOR HIGH DYNAMIC RANGE MEDICAL IMAGES

Abstract. We propose here an HDR compression method for medical images based on a windowing operator, an adaptive tone mapping operator, and the probabilistic normal-gamma model. First, we use the windowing operator based on a structural fidelity measure for optimal visualization of the input HDR medical image. Then, we transform the windowed image to the logarithm domain and split it into base and detail layers with the help of the probabilistic normal-gamma model. Base and detail layers are used to make the tone map with help the adaptive tone mapping operator. Finally, the tone mapping result is the LDR image. The proposed method has comparable quality and low computation time compared to other tone mapping operators.

Performance enhanced ripplet transform based compression method for medical images

Ripplet transforms provide efficient representation of images with all the spatial details. Since more details are extracted, the compression ratio is usually less. In this work, lossless prediction and lossy singular value decomposition of the ripplet coefficients are carried out to improve the performance of conventional ripplet transform based system. Initially, lossless prediction exploits the correlation between the image pixels and eases the process of transformation. Fast and computationally efficient randomized singular value decomposition technique is used to capture the essential information of the high frequency ripplet coefficients. The low frequency and the high frequency components are then encoded using entropy method. Experimental results show promising results for the proposed approached in terms of performance measure.

An Efficient Middle Layer Platform for Medical Imaging Archives.

Digital medical image usage is common in health services and clinics. These data have a vital importance for diagnosis and treatment; therefore, preservation, protection, and archiving of these data are a challenge. Rapidly growing file sizes differentiated data formats and increasing number of files constitute big data, which traditional systems do not have the capability to process and store these data. This study investigates an efficient middle layer platform based on Hadoop and MongoDB architecture using the state-of-the-art technologies in the literature. We have developed this system to improve the medical image compression method that we have developed before to create a middle layer platform that performs data compression and archiving operations. With this study, a platform using MapReduce programming model on Hadoop has been developed that can be scalable. MongoDB, a NoSQL database, has been used to satisfy performance requirements of the platform. A four-node Hadoop cluster has been built to evaluate the developed platform and execute distributed MapReduce algorithms. The actual patient medical images have been used to validate the performance of the platform. The processing of test images takes 15,599 seconds on a single node, but on the developed platform, this takes 8,153 seconds. Moreover, due to the medical imaging processing package used in the proposed method, the compression ratio values produced for the non-ROI image are between 92.12% and 97.84%. In conclusion, the proposed platform provides a cloud-based integrated solution to the medical image archiving problem.