Compression of medical image using vector quantization

Phanprasit, Tanasak

doi:10.1109/bmeicon.2013.6687718

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Another issue regarding medical image compression was presented by [61]. A curveletbased medical image compression using system error compensation (SEC) was proposed.…”

Section: Hardware-based Implementation Of the Compression Systemsmentioning

confidence: 99%

“…The proposed architecture [65] Referring to the current research trends regarding hardware implementation [61]- [65], most of the researcher's tendency to explore in term of throughput (speed) and PSNR, instead of power consumption and maximum frequency. These because of researchers concentrate on the quality of images and the possible time to process an architecture on the selected hardware.…”

Section: Figure 10mentioning

confidence: 99%

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Implementation Method on Medical Image Compression System: A Review

Muharam

Ahmad

2017

Jurnal Teknologi

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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.

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“…Another issue regarding medical image compression was presented by [61]. A curveletbased medical image compression using system error compensation (SEC) was proposed.…”

Section: Hardware-based Implementation Of the Compression Systemsmentioning

confidence: 99%

Section: Figure 10mentioning

confidence: 99%

Implementation Method on Medical Image Compression System: A Review

Muharam

Ahmad

2017

Jurnal Teknologi

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“…The SEC design is based on quantization and curvelet transform (CT) that decomposes the system error (E) to six scales. The results have improved 40.48%, 9.69% both in terms of bit rate and PSNR when compared to conventional method [6]. Here, VQ based image compression uses an improved partition based fuzzy clustering algorithm that is a modification of the classical fuzzy C-means method.…”

Section: Introductionmentioning

confidence: 99%

Image Compression using Different Vector Quantization Algorithms and Its Comparison

Mohanta¹,

Mohanta²

2019

IJITEE

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Image compression techniques are presented in this paper which can be used for storage and transmission of digital lossy images. It is mostly important in both multimedia and medical field to store huge database and data transfer. Medical images are used for diagnosis purposes. So, vector quantization is a novel method for lossy image compression that includes codebook design, encoding and decoding stages. Here, we have applied different lossy compression techniques like VQ-LBG (Vector quantization- Linde, Buzo and Gray algorithm), DWT-MSVQ (Discrete wavelet transform-Multistage Vector quantization), FCM (Fuzzy c-means clustering) and GIFP-FCM (Generalized improved fuzzy partitions-FCM) methods on different medical images to measure the qualities of compression. GIFP-FCM is an extension of classical FCM and IFP-FCM (Improved fuzzy partitions FCM) algorithm with a purpose to reward hard membership degree. The presentation is assessed based on the effectiveness of grouping output. In this method, a new objective function is reformulated and minimized so that there is a smooth transition from fuzzy to crisp mode. It is fast, easy to implement and has rapid convergence. Thus, the obtained results show that GIFP-FCM algorithm gives better PSNR performance, high CR (compression ratio), less MSE (Mean square error) and less distortion as compared to other above used methods indicating better image compression.

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Image Compression Based on a Hybrid Wavelet Packet and Directional Transform (HW&DT) Method

Latha

Fathima

2020

Lecture Notes in Networks and Systems

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Compression of medical image using vector quantization

Cited by 3 publications

References 11 publications

Implementation Method on Medical Image Compression System: A Review

Implementation Method on Medical Image Compression System: A Review

Image Compression using Different Vector Quantization Algorithms and Its Comparison

Image Compression Based on a Hybrid Wavelet Packet and Directional Transform (HW&DT) Method

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