High Efficient Haar Wavelets for Medical Image Compression

Zanaty, E. A.; Ibrahim, Sherif M.

doi:10.1007/978-3-030-31129-2_50

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Studies [7]- [9], [12], [13], [15]- [17], [19]- [22], [24]- [31], and [35] used transformations (DWT, CT, IWT, and curvelet transform). These algorithms are characterised by their speed and their ability to segment the image data to the lowest level to reach the surplus and remove it without affecting the image after its retrieval.…”

Section: Discussionmentioning

confidence: 99%

“…For the input image, three different medical modalities were used: CT, MRI and US images. Zanaty and Ibrahim [31] suggested an improved algorithm for highly efficient Haar wavelets (HEHW). It begins by dividing the original image into submatrices of 2*2, then the coefficients, obtained by working on submatrices instead of rows and columns in the original image, are transformed by the wavelets and the resulting coefficients for sub-matrices are re-computed.…”

Section: Transformation Techniquesmentioning

confidence: 99%

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Significant medical image compression techniques: a review

Abduljaleel

Khaleel

2021

TELKOMNIKA

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Transformation Techniquesmentioning

confidence: 99%

Significant medical image compression techniques: a review

Abduljaleel

Khaleel

2021

TELKOMNIKA

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“…More details are in Figure 7. The results of the compression time test on medical images with three wavelets as shown in Figure 7, that the fastest average processing time for CT-scan medical images occurs in Haar wavelets [39]. In general, compression time for all wavelet methods, CT-scan medical images compress the fastest.…”

Section: G Bit Ratementioning

confidence: 94%

Comparison of the Packet Wavelet Transform Method for Medical Image Compression

Atmaja,

Triadi,

Astawa

et al. 2023

JOIV : Int. J. Inform. Visualization

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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.

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High Efficient Haar Wavelets for Medical Image Compression

Cited by 2 publications

References 8 publications

Significant medical image compression techniques: a review

Significant medical image compression techniques: a review

Comparison of the Packet Wavelet Transform Method for Medical Image Compression

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