scholarly journals Algorithm development and hardware implementation for medical image compression system: a review

2020 ◽  
Vol 18 (3) ◽  
pp. 1331
Author(s):  
Noor Huda Ja’afar ◽  
Afandi Ahmad
Keyword(s):  
Performance Evaluation ◽  
Medical Imaging ◽  
Image Compression ◽  
Medical Image ◽  
Hardware Implementation ◽  
Medical Image Compression ◽  
Algorithm Development ◽  
Compression System ◽  
Hardware Implementations ◽  
Software And Hardware

<span>In the high-tech world, medical imaging is very important to diagnose and analyze illness inside human body. The increasing number of patients annually has continuously growth the amount of medical imaging data generated and directly causes a demand for data storage. Generally, medical images are rich with data, where these data are important for diagnosing purpose. However, some of the data represents redundant information and sometimes can be discarded. Thus, the research area on medical image compression dealing with three-dimensional (3-D) modalities need to be given more attention and exploration. The algorithm development using wavelet transform with software implementation are the famous topics explored among researchers, whilst fewer works have been done in utilizing curvelet transform in medical image compression. Along with that, very limited hardware implementation of 3-D medical image compression is discovered. In term of performance evaluation, most of the previous works conducted objective test compared with subjective test. To fill in this gap, medical image compression system will be reviewed, with the aim to identify the recent method used in medical image compression system. This paper thoroughly scrutinizes the recent advances in medical image compression mainly in terms of compression method, algorithm development with software and hardware implementations and performance evaluation. In conclusion, the overall picture of the medical image compression landscape, where most of the researchers more focused on algorithm development or software implementations without having the combination of software and hardware implementations.</span>

scholarly journals IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW

2017 ◽  
Vol 79 (7) ◽  
Author(s):  
Azlan Muharam ◽  
Afandi Ahmad
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Medical Image ◽  
Rapid Development ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Subjective Test ◽  
Medical Image Compression ◽  
Research Issues ◽  
Software And Hardware

The rapid development of medical imaging and the invention of various medicines have benefited mankind and the whole community. Medical image processing is a niche area concerned with the operations and processes of generating images of the human body for clinical purposes.  Potential areas such as image acquisition, image enhancement, image compression and storage, and image based visualization also include in medical image processing analysis. Unfortunately, medical image compression dealing with three-dimensional (3-D) modalities still in the pre-matured stage. Along with that, very limited researchers take a challenge to apply hardware on their implementation. Referring to the previous work reviewed, most of the compression method used lossless rather than lossy. For implementation using software, MATLAB and Verilog are the famous candidates among researchers. In term of analysis, most of the previous works conducted objective test compared with subjective test. This paper thoroughly reviews the recent advances in medical image compression mainly in terms of types of compression, software and hardware implementations and performance evaluation. Furthermore, challenges and open research issues are discussed in order to provide perspectives for future potential research. In conclusion, the overall picture of the image processing landscape, where several researchers more focused on software implementations and various combinations of software and hardware implementation.  

Survey of Medical Image Compression Techniques and Comparative Analysis

2017 ◽  
pp. 1165-1198
Author(s):  
P. Geetha
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Digital Imaging ◽  
Medical Image ◽  
Satellite Communications ◽  
Digital Data ◽  
High Definition ◽  
Medical Image Compression ◽  
High Definition Television ◽  
Digital Storage

Today digital imaging is widely used in every application around us like Internet, High Definition TeleVision (HDTV), satellite communications, fax transmission, and digital storage of movies and more, because it provide superior resolution and quality. Recently, medical imaging has begun to take advantage of digital technology, opening the way for advanced medical imaging and teleradiology. However, medical imaging requires storing, communicating and manipulating large amounts of digital data. Applying image compression reduces the storage requirements, network traffic, and therefore improves efficiency. This chapter provides the need for medical image compression; different approaches to image compression, emerging wavelet based lossy-lossless compression techniques, how the existing recent compression techniques work and also comparison of results. After completing this chapter, the reader should have an idea of how to increase the compression ratio and at the same time maintain the PSNR level compared to the existing techniques, desirable features of standard compression techniques such as embededness and progressive transmission, how these are very useful and much needed in the interactive teleradiology, telemedicine and telebrowsing applications.

3D Medical Images Compression

2021 ◽  
pp. 1046-1067
Author(s):  
Mohamed Fawzy Aly ◽  
Mahmood A. Mahmood
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Medical Data ◽  
The Body ◽  
Medical Image Compression ◽  
Diagnostic Data ◽  
Digital Representations ◽  
And Storage

Medical images are digital representations of the body. Medical imaging technology has improved tremendously in the past few decades. The amount of diagnostic data produced in a medical image is vast and as a result could create problems when sending the medical data through a network. To overcome this, there is a great need for the compression of medical images for communication and storage purposes. This chapter contains an introduction to compression types, an overview of medical image modalities, and a survey on coding techniques that deal with 3D medical image compression.

Survey of Medical Image Compression Techniques and Comparative Analysis

2014 ◽  
pp. 327-356
Author(s):  
P. Geetha
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Digital Imaging ◽  
Medical Image ◽  
Satellite Communications ◽  
Digital Data ◽  
High Definition ◽  
Medical Image Compression ◽  
High Definition Television ◽  
Digital Storage

Today digital imaging is widely used in every application around us like Internet, High Definition TeleVision (HDTV), satellite communications, fax transmission, and digital storage of movies and more, because it provide superior resolution and quality. Recently, medical imaging has begun to take advantage of digital technology, opening the way for advanced medical imaging and teleradiology. However, medical imaging requires storing, communicating and manipulating large amounts of digital data. Applying image compression reduces the storage requirements, network traffic, and therefore improves efficiency. This chapter provides the need for medical image compression; different approaches to image compression, emerging wavelet based lossy-lossless compression techniques, how the existing recent compression techniques work and also comparison of results. After completing this chapter, the reader should have an idea of how to increase the compression ratio and at the same time maintain the PSNR level compared to the existing techniques, desirable features of standard compression techniques such as embededness and progressive transmission, how these are very useful and much needed in the interactive teleradiology, telemedicine and telebrowsing applications.

scholarly journals 3D Medical image compression using the quincunx wavelet coupled with SPIHT

2020 ◽  
Vol 18 (2) ◽  
pp. 821
Author(s):  
Benlabbes Haouari
Keyword(s):  
Wavelet Transform ◽  
Medical Imaging ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Digital Technologies ◽  
Visual Quality ◽  
Medical Image Compression ◽  
Coding Scheme

<p>Medical imaging is a growing field due to the development of digital technologies that produce 3D and even 4D data. The counterpart to the resolution offered by these voluminal images resides in the amount of gigantic data, hence the need for compression. This article presents a new coding scheme dedicated to 3D medical images. The originality of our approach lies in the application of the Quinqunx wavelet transform coupled with the SPIHT encoder on a database of medical images. This approach achieves much higher compression rates, while maintaining a very acceptable visual quality.</p>

3D Medical Images Compression

2018 ◽  
pp. 259-279
Author(s):  
Mohamed Fawzy Aly ◽  
Mahmood A. Mahmood
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Medical Data ◽  
The Body ◽  
Medical Image Compression ◽  
Diagnostic Data ◽  
Digital Representations ◽  
And Storage

Medical images are digital representations of the body. Medical imaging technology has improved tremendously in the past few decades. The amount of diagnostic data produced in a medical image is vast and as a result could create problems when sending the medical data through a network. To overcome this, there is a great need for the compression of medical images for communication and storage purposes. This chapter contains an introduction to compression types, an overview of medical image modalities, and a survey on coding techniques that deal with 3D medical image compression.

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