Medical Image Compression Techniques Research Articles

DermCompressNet: integrated CD-ConvNet and discrete cosine transform for dermoscopic images compression

AbstractTelemedicine has a critical role in healthcare by supporting the information exchange between the patients and the physicians as well as between the physicians for consultation. Such technology has urgent requirements for storage reduction and efficient use of the transmission channel bandwidth. Such requirements can be achieved efficiently via developing accurate medical image compression techniques. For accurate diagnosis, lossless compression methods are recommended. However, the tradeoff between the compression ratio (CR) and the preservation of the image quality is still challenging. On the other hand, the advantages of the convolutional neural networks inspired this work to design a novel proposed system for dermoscopic image compression based on the integration of the compression direction-ConvNet (CD-ConvNet) and decompression direction-ConvNet (DD-ConvNet) with discrete cosine transform (DCT) and Huffman coding, called DermCompressNet. To reconstruct a high-quality image at the receiver, the inverse processes using the DD-ConvNet network were followed. The proposed system was evaluated by measuring several image quality metrics, namely the mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), along with compression quality metrics, namely CR, and computational time (CT). The experimental results achieved 34.6 dB, 2.5, 0.85, and 56% of PSNR, MSE, SSIM, and CR, respectively. A comparison studies with the JPEG and state-of-the-art methods proved the superiority of the proposed system, showing23%, 16%, 4.3%, and 1.8% improvements in the PSNR, MSE, SSIM, and CR, respectively, compared to the JPEG.

Optimal machine learning model based medical image compression techniques for smart healthcare

Optimal machine learning model based medical image compression techniques for smart healthcare

An efficient medical image compression technique for telemedicine systems

The medical practitioners primarily used medical images to reveal abnormalities in the internal critical organs and structures of body covered by the bones and the skin. Main application of medical imaging is to perform medical diagnosis from the image features extracted. Processing these images are very much required for assessing the patient’s condition. However, long-term monitoring of the patient using certain medical imaging technologies produces enormous volumes of data everyday. There is a need to compress the data to reduce redundancies and speed up the acquisition process, making them suitable for efficient transmission and analysis. Recently Compressed Sensing (CS) has been widely used for image compression at high speed with fewer samples. High-quality reconstruction using conventional CS and Block based CS (BCS) is a matter of utmost concern as they follow the random selection of samples. This could be overcome by adaptively selecting samples from various image regions using Adaptive Block Compressed Sensing (ABCS). This paper proposes Coefficient Mixed Thresholding based ABCS (CMT-ABCS) for compressing different medical images with a high compression ratio. The experimental outcomes exhibit a noteworthy improvement in the proposed method’s performance metrics when compared to other state-of-the-art approaches. There is a increase in PSNR of 5–10 dB, SSIM of 0.1–0.2 with NCC values closer to 1 and NAE values closer to 0. At low sampling rate, the reconstruction was greatly enhanced with only around 10% measurements/samples.

Significant medical image compression techniques: a review

Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms.

A near lossless three-dimensional medical image compression technique using 3D-discrete wavelet transform

In the field of medical image processing, there are several imaging modalities that emerged and produced high quality of images that imposes to greater amount of storage space as well as bandwidth for transmission. Therefore, there is always a great need to develop compression techniques on such images. Moreover, they produce three-dimensional images by sequence of 2D slices and process each as like 2D image. Therefore, it is essential that every medical image processing operation needs to converge on 3D space (three-dimensional image). In this paper, we provide the wavelet-based image compression technique that can be directly applied on the 3D images itself. We apply 3D DWT on a 3D volume and the resultant coefficients are taken to further compression process (like thresholding, entropy encoding). The inverse processes are performed for decompression to reconstruct the images. The effects of the results based on different wavelets are assessed and the results of lossy mode are compared in terms to lossless mode.

A near lossless three-dimensional medical image compression technique using 3D-discrete wavelet transform

In the field of medical image processing, there are several imaging modalities that emerged and produced high quality of images that imposes to greater amount of storage space as well as bandwidth for transmission. Therefore, there is always a great need to develop compression techniques on such images. Moreover, they produce three-dimensional images by sequence of 2D slices and process each as like 2D image. Therefore, it is essential that every medical image processing operation needs to converge on 3D space (three-dimensional image). In this paper, we provide the wavelet-based image compression technique that can be directly applied on the 3D images itself. We apply 3D DWT on a 3D volume and the resultant coefficients are taken to further compression process (like thresholding, entropy encoding). The inverse processes are performed for decompression to reconstruct the images. The effects of the results based on different wavelets are assessed and the results of lossy mode are compared in terms to lossless mode.

Medical Image Compression Techniques with Wavelet Discrete Transformation and Entropy Encoding

Medical imaging is a presentment of human organ parts. Medical imaging is saved on a film; therefore, it needs a big saving quota. Compressing is a process to remove redundancy from a piece of information without reducing its quality. This study recommended compressed medical image with DWT (Discrete Wavelet Transform) with adaptive threshold added and entropy copying with the Run Length Encoding (RLE) coding. This study is comparing several parameters, such as compressed ratio and compressed image file size, and PSNR (Peak Signal to Noise Ratio) for analyzing the quality of reconstructive image. The study showed that the comparison of rate, compressed ratio, and PSNR tracing of Haar and Daubechies doesn’t have a significant difference. Comparison of rate, compressed ratio, and PSNR tracing on the hard and soft threshold is the rate of the sold threshold is lower than the hard threshold. The optimal outcome of this study is to use a soft threshold.

Metaheuristic FIR filter with game theory based compression technique- A reliable medical image compression technique for online applications

In image processing, there are many inventions results in processing the images effectively suitable for applications like change detection, SAR image analysis, Television i.e. for compressing videos for effective transmission etc. In Social network, exchange of bulk information needs an effective compression technique to compress, transmit, decompress and reconstruct the original quality of source information. Compression technique can reduce the storage deficiency, energy consumption. In this proposed methodology, an effective Finite Impulse Response filter designed with meta-heuristic techniques-a game theory-based approach provides a compromising data compression ratio for standard input images. Effective quality indicators such as compression ratio, Mean Square Error stands as proof for the implications of the anticipated technique in medical applications as it affords extraordinary results for compression of Ultrasound and MRI images, thus providing a pathway for a trans receiving data with minimum space required.

Medical Image Compression Based on Combining Region Growing and Wavelet Transform

Medical data grows very fast and hence medical institutions need to store high volume of data about their patients. Medical images are one of the most important data types about patients. As a result, hospitals have a high volume of images that require a huge storage space and transmission bandwidth to store these images. Most of the time transmission bandwidth is not sufficient to storing and transmit all the image data with the required efficiency. Image compression is the process of encoding information using fewer bits than an un-encoded representation using specific encoding schemes. Compression is useful because it helps to reduce the consumption of expensive resources, such as storage space or transmission bandwidth (computing). In this paper, a medical image compression technique based on combining region growing and wavelets algorithms was introduced. A region growing algorithm is used to simply partitioning the image into two parts foreground and background depending on the intensity values. Then, wavelets methods applied on foreground regions including important regions. These regions are compressed lossless to keep the appearance of the image as intact while making the simplifications and the other region is lossy compressed to reduce the file size, leading to that the overall compression ratio gets better and the reconstructed image seems like the original one. To prove the capability of the proposed algorithm, different four image structures from X-Ray, Computed tomography CT and Magnetic resonance imaging MRI types are tested.

Selection of Vanishing moments of Symlet and Coiflet Wavelets on CT Images

The main aim of the work is to develop image compression algorithms with high quality and compression ratio. The objective also includes finding out an best algorithm for medical image compression techniques. The objective is also alert towards the choice of the developed image compression algorithm, which do not modify the characterization behavior of the image. In this paper, image compression algorithm based on discrete symlet and coiflet wavelet transform is implemented for decomposing the image. The selection of different vanishing moments are discussed based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The optimum moments for compression are also chosen based on the results.

An Implementation on Pattern Creation and Fixed Length Huffman’s Compression Techniques for Medical Images

An Implementation on Pattern Creation and Fixed Length Huffman’s Compression Techniques for Medical Images

A Hybrid Medical Image Compression Techniques for Lung Cancer

Objectives: This study focuses on Image compression and compares different methods. Methods/Statistical Analysis: In this work we simulated four image compression methods. The first method is focused on Karhunen-Loeve Transforms (KLT), second method is focused on Walsh-Hadamard Transforms (WHT), third method based on FFT and fourth one is proposed sFFT. Findings: The experimental outcomes are compared with the different quality of parameters applying on numerous lung cancers CT scan images. The Proposed SFFT method algorithm was given better results like Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Mean Square Error (MSE) and Compression Ratio (CR) are compare to other Transform methods. Application /Improvement: The Proposed SFFT technique gives improved result compared with other methods in all evaluation measures.

Optimal Filter Design of Wavelet Transform to an Adaptive Medical Image Compression Technique Using Genetic Group Search Optimizer

Optimal Filter Design of Wavelet Transform to an Adaptive Medical Image Compression Technique Using Genetic Group Search Optimizer

An improved medical image compression technique with lossless region of interest

Hospitals and medical centers produce an enormous amount of digital medical images every day, which are used for different purposes such as surgical and diagnostic plans. The ease of storing and transmission of digital medical images is a boon to patients and medical professionals. Due to the large volume of images, image compression is required to reduce the redundancies in image and represents it in shorter manner for efficient archiving and transmission of images. However, compressing digital medical images as the region of interest for diagnosis is generally small when compared to the whole image. Lossless compression techniques compress without loss of any information but have low compression rate, and lossy compression techniques can compress at high compression ratio but with a slight loss of data. Using lossless techniques in medical image does not give enough advantage in transmission and storage and lossy techniques may lose crucial data required for diagnosis. In this paper, an improved medical image compression technique based on region of interest (ROI) is proposed to maximize compression. The image is firstly divided into two parts: ROI regions and non-ROI regions. Lossless compression algorithm is then applied to the marked area of ROI, and image restoration technique and the wavelet-based lossy compression algorithm are utilized to the other area of the image. Finally, a set of experiments is designed to assess the effectiveness of the proposed compression method.

Adaptive Thresholding Based Medical Image Compression Technique Using Haar Wavelet Based Listless SPECK Encoder and Artificial Neural Network

Adaptive Thresholding Based Medical Image Compression Technique Using Haar Wavelet Based Listless SPECK Encoder and Artificial Neural Network

IJIREEICE - Electrical, Electronics, Instrumentation and Control

Image compression plays a crucial role in medical imaging allowing efficient storage and transmissions by reducing the amount of data required to represent the digital image. The main goal is to achieve higher compression ratios and minimum degradation in quality. To decrease the storage space, the use of different compression techniques is justified by some medical imaging modalities generate the volume that of data which will be increasing. Different medical images like X-ray angiograms, magnetic resonance images, Ultrasound and computed Tomography are used in the medical image compression techniques. In medical applications it is required to conserve the diagnostic validity of the image requires the use of lossless compression methods, producing low compression factors. For medical data, lossless compression is preferred to the greater gains of lossy compression, in the interest of accuracy. . A set of experiment has been performed for the analysis of the proposed work on the several medical images and it has been observed that the DWT, DCT and Huffman coding has higher compression ratio than the hybrid model. The proposed method gives better quality of image that includes high PSNR and CR as well as low MSE. The proposed medical DICOM images compression scheme is based on Hybrid DWT, DCT and Huffman coding techniques.

An Effective Selection of DCT and DWT Coefficients for an Adaptive Medical Image Compression Technique Using Multiple Kernel FCM

Over the last two decades, great developments have been made in image compression approaches driven by a growing demand for storage and transmission of visual information. However, a number of publications have demonstrated good results of the compressed domain approach to image analysis. Nevertheless, very little work has been carried out on ROI based compression. In this paper, we have proposed an adaptive approach to compress the image without lossless version using selection of DCT and DWT coefficients and MKFCM. Our proposed approach consists of three stages, (i) Region segmentation (ii) Image compression (iii) Image decompression. At first, the input medical image is segmented by ROI, Non-ROI and background using MKFCM. Subsequently, the ROI region compressed by DCT and SPIHT coding and Non-ROI region is compressed by DWT and Huffman coding. The non-relevant regions are directly converted to zero. From the compressed regions like as ROI, Non-ROI and background, finally we obtain compression ratio to evaluate the proposed approach. Then, in decompression stage, the original medical image is extracted using the devised procedure. We can also see that our proposed image compression approach have outperformed by having better compression ratio of 4.6446 when compared existing technique only achieved 4.4326

A Novel Medical Image Compression Technique based on Structure Reference Selection using Integer Wavelet Transform Function and PSO Algorithm

The utility of medical image increases due to serious disease prediction such as brain stroke and lung cancer. The size of medical image required large amount of memory and bandwidth for storage and transmission. For the lossless compression of medical image some standard algorithm are used such as JPEG, composite and Wavelet based. These entire algorithms perform good compression ratio and PSNR value. But the redundant structure of medical image reduces the compression ratio and decreases the value of PSNR and suffered quality of medical image compression. In this paper we proposed a structure reference selection process for collecting redundant frame of structure for compression. The collection of redundant frame structure is performed by particle swarm optimization algorithm. Particle swarm optimization is searching technique. For the generation of structure of frame we used integer wavelet transform function. The proposed algorithm is implemented through MATLAB software and is compared with composite algorithm, SOM algorithm and JPEG compression technique. General Terms Integer wavelet transforms, Reference Frame, Image Compression et. al.

A TWO COMPONENT MEDICAL IMAGE COMPRESSION TECHNIQUES FOR DICOM IMAGES

To meet the demand for high speed transmission of image, efficient image storage, remote treatment an efficient image compression technique is essential. Wavelet theory has great potential in medical image compression. Most of the commercial medical image viewers do not provide scalability in image compression. This paper discusses a medical application that contains a viewer for digital imaging and communications in medicine (DICOM) images as a core module. Progressive transmission of medical images through internet has emerged as a promising protocol for teleradiology applications. The major issue that arises in teleradiology is the difficulty of transmitting large volume of medical data with relatively low bandwidth. Recent image compression techniques have increased the viability by reducing the bandwidth requirement and cost-effective delivery of medical images for primary diagnosis. This paper presents an effective algorithm to compress and reconstruct Digital Imaging and Communications in Medicine (DICOM) images. DICOM is a standard for handling, storing, printing and transmitting information in medical imaging. These medical images are volumetric consisting of a series of sequences of slices through a given part of the body. DICOM image is first decomposed by Haar Wavelet Decomposition Method. The wavelet coefficients are encoded using Set Partitioning in Hierarchical Trees (SPIHT) algorithm. Discrete Cosine Transform (DCT) is performed on the images and the coefficients are JPEG coded. The quality of the compressed image by different method are compared and the method exhibiting highest Peak Signal to Noise Ratio (PSNR) is retained for the image. The performance of the compression of medical images using the above said technique is studied with the two component medical image compression techniques.

Survey on recent techniques in volumetric medical image compression

Recent advances in medical imaging technology have led to ubiquitous use of medical images for diagnosis. As a result, huge amount of medical image data is generated on a daily basis in hospitals. This data needs to be stored digitally for efficient diagnosis, transmission and for future study and follow up. This requires a large amount of storage space which is especially true for three–dimensional (3–D) medical data formed by combining medical images which are in slices. Digital Imaging and Communications in Medicine (DICOM) protocol has created to be the international standard of using various medical imaging modality for archiving patient images digitally. To make efficient utilisation of storage space and bandwidth, medical image data sets need to be compressed efficiently. Picture Archieving and Communications (PACS) technology have been developed in an attempt to provide economical storage, rapid retrieval of images, access to images acquired with multiple modalities and simultaneous access at multiple workstations. Therefore compression technique in PACS is more important part than the way of storage and transmission. In this paper recent techniques in volumetric medical image coding is introduced and a wide variety of lossless medical image compression techniques have been discussed.