A Metaheuristic Approach for Tetrolet-Based Medical Image Compression

Saravanan, S.; Juliet, Sujitha

doi:10.4018/jcit.20220401.oa3

Cited by 5 publications

(8 citation statements)

References 36 publications

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“…To demonstrate the proposed compression method's superiority, its efficiency is compared with other well-known techniques in light of PSNR, CR and SSS. The previous methods considered for comparison are PSO-based thresholding BWT-MTF [9] tetrolet [10] QFOM [11] optimized RNN [12] and SPIHT-BAT [13] because all the previous models process the MRI images. Ahilan et al [7] used multilevel thresholding of PSO to extract ROI.…”

Section: Discussion On the Resultsmentioning

confidence: 99%

“…After compression, the data is encoded with HE. Maheswari and Raghavan [10] used the tetrolet method to compress medical images. Magar and Sridharan [11] compressed the ROI region using quasifractal and non-ROI with OM.…”

Section: Discussion On the Resultsmentioning

confidence: 99%

“…Maheswari and Raghavan [10] designed a compression algorithm for medical images. In this work, tetrolet was applied to divide an image into multiple sub-images.…”

Section: Review Of Past Approachesmentioning

confidence: 99%

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A novel compression methodology for medical images using deep learning for high-speed transmission

Navaneethakrishnan,

Shanmugam

2024

IJRES

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Medical imaging is a rapidly growing field having a high impact on the early detection, diagnosis and surgical planning of diseases. Several imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) imaging generate a higher volume of data, necessitating additional storage and communication requirements. Hence, image compression is utilized in medical field to reduce redundancy and alleviate memory and bandwidth issues. This paper presents a novel deep learning-based compression method to reduce the size of medical images. This method employs a deep convolutional neural network for learning compact representations of medical images, then coded by a Huffman encoder. The compression process is reversed to reconstruct the original image. Several tests are conducted to compare the results with other wellknown compression methods. The proposed model achieved a mean peak signal-to-noise ratio (PSNR) of 42.82 dB with storage space saving (SSS) of 96.15% for CT, 43.88 dB with SSS of 96.25% for MRI, 46.29 dB with SSS of 96.07% for US and 43.51 dB with SSS of 96.95% for X-ray images. The findings showed that the proposed compression technique could greatly compress the image size, saving storage space, facilitating better transmission and preserving critical diagnostic information.

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Section: Discussion On the Resultsmentioning

confidence: 99%

Section: Discussion On the Resultsmentioning

confidence: 99%

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A novel compression methodology for medical images using deep learning for high-speed transmission

Navaneethakrishnan,

Shanmugam

2024

IJRES

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“…The bigger + is, the smaller the co-zero set (0, 1 2 / ) is where as the Fourier consist of only one choice of basis function of basis function (1.cos 3 , sin 3 ) Saravanan [14].…”

Section: Definition Of Wavelet Eclay [6]mentioning

confidence: 99%

Frequency Domain Analysis of Nigeria All Share and Capital Index from 1989-2010 (A Case Study of Nigerian Stock Market)

Osaro¹,

Suleiman²

2022

PAMJ

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In recent years the method of wavelet analysis has been opened to researchers. It is the analyses of data at different level of decomposition and can capture the characteristics of data series in all decomposition level. In this research work, data was collected on the Nigerian stock index for All Share and Capital market indexes . The data were analyzed by wavelet method to detect the aberrant observations (AOs) over the period under study for the two indexes. Akaike information criterion (AIC) was also used to detect the 'best model' for the two indexes using some distributions. A total of seventeen and eleven AOs were detected from the original data collected on All Share and Capital Market indexes respectively. In the first, second and third resolutions, a total of four, two and two AOs were detected from the All Share index, while a total of five, four and three AOs were detected from that of Capital Market index. The results obtained showed the AOs detected in the analysis of the original data maintain the same or closely the same positions as that obtained from the analysis of the decomposed data for the two stock indexes. It was observed that the index of stocks in March, July and December are more and less in February, March, and November for the two indexes. The AIC results show that, the Cauchy distribution has the smallest AIC values among the distributions used, which means is the 'best model'.

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“…defense [1][2][3][4][5][6]. In the classical definition, image compression refers to reducing the size of an image without degrading its quality [2].…”

mentioning

confidence: 99%

A State Table SPHIT Approach for Modified Curvelet-based Medical Image Compression

Ja'afar,

Ahmad,

Safie

2024

Int. J. Onl. Eng.

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Medical imaging plays a significant role in clinical practice. Storing and transferring a large volume of images can be complex and inefficient. This paper presents the development of a new compression technique that combines the fast discrete curvelet transform (FDCvT) with state table set partitioning in the hierarchical trees (STS) encoding scheme. The curvelet transform is an extension of the wavelet transform algorithm that represents data based on scale and position. Initially, the medical image was decomposed using the FDCvT algorithm. The FDCvT algorithm creates symmetrical values for the detail coefficients, and these coefficients are modified to improve the efficiency of the algorithm. The curvelet coefficients are then encoded using the STS and differential pulse-code modulation (DPCM). The greatest amount of energy is contained in the coarse coefficients, which are encoded using the DPCM method. The finest and modified detail coefficients are encoded using the STS method. A variety of medical modalities, including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), are used to verify the performance of the proposed technique. Various quality metrics, including peak signal-to-noise ratio (PSNR), compression ratio (CR), and structural similarity index (SSIM), are used to evaluate the compression results. Additionally, the computation time for the encoding (ET) and decoding (DT) processes is measured. The experimental results showed that the PET image obtained higher values of the PSNR and CR. The CT image provides high quality for the reconstructed image, with an SSIM value of 0.96 and the fastest ET of 0.13 seconds. The MRI image has the shortest DT, which is 0.23 seconds.

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A Metaheuristic Approach for Tetrolet-Based Medical Image Compression

Cited by 5 publications

References 36 publications

A novel compression methodology for medical images using deep learning for high-speed transmission

A novel compression methodology for medical images using deep learning for high-speed transmission

Frequency Domain Analysis of Nigeria All Share and Capital Index from 1989-2010 (A Case Study of Nigerian Stock Market)

A State Table SPHIT Approach for Modified Curvelet-based Medical Image Compression

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